**4. Application of AI in prosthetics and orthotics**

Implementation of artificial intelligence in controlling prostheses has increased drastically and thus enables the amputee to operate the prosthesis more desirably. Adaptive controlling would enable a system to perform closer to the desired output by adjusting the input with the help of a feedback system. Recently, a mindcontrolled limb (type of myoelectric controlling) was introduced as the latest advancement in the artificial intelligence-aided control system. A joint project between the Pentagon and Johns Hopkins Applied Physics Laboratory (APL) has come up with a modular prosthetic limb which would be fully controlled by sensors implanted in the brain, and would even restore the sense of touch by sending electrical impulses from the limb back to the sensory cortex [25]. Chang et al. (2009) proposed a multilayer artificial neural network (ANN)-based model to discover the essential correlation between the intrinsic impaired neuromuscular activities of people with spina bifida (SB) and their extrinsic gait behaviors [26]. The application of AI in prosthetics and orthotics is divided into various subparts according to the involvement of the region that get affected i.e. Lower extremity prosthesis and Orthosis, Upper extremity Orthosis and prosthesis, and rehabilitation aids like motorized mobility devices.

## **4.1 AI in upper extremity prosthesis and orthosis**

The artificial Intelligence in upper extremity prosthesis used as direct control and indirect control from the neural network by various signal, sensor, controller and algorithm. The control signals are coming from the human in the two form for operation of upper extremity prosthesis i.e. electromyography (EMG) and Electroencephalogram (EEG). Prior attempts at voluntary control of the elements of prosthesis have focused on the use of electromyography (EMG) signals from muscle groups that remain under voluntary control. Most of this work has centered on control systems for upper extremity prostheses. The first commercialized powered hand myoelectric prosthesis was introduced by USSR in 1960 [27]. The advancement in EMG control myoelectric prosthesis was with use of EMG pattern recognition based control strategy [28]. This approach allows the user

**25**

**Figure 6** [33].

*Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation*

to control the prosthesis with multiple degrees of freedom. The most advanced and developed neural machine interface technology was TMR or targeted muscle

The conventional Electromyography (EMG) technique uses bipolar surface electrodes, placed over the muscle belly of the targeted group of muscles. The electrodes are noninvasive, inexpensive, and readily incorporated into the socket of the prosthesis. These surface electrode have limitations like inability to record the signal from different muscle group at a time, inconsistency in signal magnitude and frequency, due to change in skin electrode interface associated in physiological and environmental modifications and also the EMG signals may encounter noise and interference from other tissues. Apart from these limitations it is easy to use by amputee and risk free. The amplitude of the EMG signal is mostly proportional to the contraction of the remaining muscle. To enhance the quality of the signal the Myoelectric control of prosthesis or other system utilizes the electrical action potential of the residual limb's muscles that are emitted during muscular contractions. These emissions are measurable on the skin surface at a microvolt level. The emissions are picked up by one or two electrodes and processed by band-pass filtering, rectifying, and low-pass filtering to get the envelope amplitude of EMG signal for use as control signals to the functional elements of the prosthesis. The myoelectric emissions are used only for control. In simultaneous control (muscle co contraction) and proportional control (fast and slow muscle contraction) controls the two different mode from wrist to

The advance method over the conventional technique of EMG signal which replace the complicated mode of switching is the pattern recognition. This new control approach is stranded on the assumption that an EMG pattern contains information about the proposed movements involved in a residual limb. Using a technique of pattern classification, a variety of different intended movements can be identified by distinguishing characteristics of EMG patterns. Once a pattern has been classified, the movement is implemented through the command sent to a prosthesis controller. EMG pattern-recognition-based prosthetic control method involves performing EMG measurement (to capture reliable and consistent myoelectric signals), feature extraction (to recollect the most important discriminating information from the EMG), classification (to predict one of a subset of intentional movements), and multifunctional prosthesis control (to implement the operation of prosthesis by the predicted class of movement) [30]. EMG pattern recognition

In pattern recognition control for a multifunctional prosthesis, multi-channel myoelectric recordings are needed to capture enough myoelectric pattern information. The number and placement of electrodes would mainly depend on how many classes of movements are demanded in a multi-functional prosthesis and how many residual muscles of an amputee are applicable for myoelectric control. For myoelectric transradial prostheses, the EMG signals are measured from residual muscles with a number of bipolar electrodes (8-16) which are placed on the circumference of the remaining forearm in which 8 of the 12 electrodes were uniformly placed around the proximal portion of the forearm and the other 4 electrodes were positioned on the distal end. A large circular electrode was placed on the elbow of the

For acquisition of EMG signal 50 Hz-60 Hz can be used to remove or reduce more low-frequency to increase the control stability of a multifunctional myoelectric prosthesis [32]. EMG feature extraction is performed on windowed EMG data, all EMG recordings channels are segmented into a series of analysis windows either with or without time overlap (WL (window length) is 100-250 ms) shown in

block diagram of Trans radial prosthesis shown in **Figure 5**.

*DOI: http://dx.doi.org/10.5772/intechopen.93903*

reinnervation [29].

terminal device and vice versa.

amputated arm as a ground [31].

#### *Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation DOI: http://dx.doi.org/10.5772/intechopen.93903*

to control the prosthesis with multiple degrees of freedom. The most advanced and developed neural machine interface technology was TMR or targeted muscle reinnervation [29].

The conventional Electromyography (EMG) technique uses bipolar surface electrodes, placed over the muscle belly of the targeted group of muscles. The electrodes are noninvasive, inexpensive, and readily incorporated into the socket of the prosthesis. These surface electrode have limitations like inability to record the signal from different muscle group at a time, inconsistency in signal magnitude and frequency, due to change in skin electrode interface associated in physiological and environmental modifications and also the EMG signals may encounter noise and interference from other tissues. Apart from these limitations it is easy to use by amputee and risk free. The amplitude of the EMG signal is mostly proportional to the contraction of the remaining muscle. To enhance the quality of the signal the Myoelectric control of prosthesis or other system utilizes the electrical action potential of the residual limb's muscles that are emitted during muscular contractions. These emissions are measurable on the skin surface at a microvolt level. The emissions are picked up by one or two electrodes and processed by band-pass filtering, rectifying, and low-pass filtering to get the envelope amplitude of EMG signal for use as control signals to the functional elements of the prosthesis. The myoelectric emissions are used only for control. In simultaneous control (muscle co contraction) and proportional control (fast and slow muscle contraction) controls the two different mode from wrist to terminal device and vice versa.

The advance method over the conventional technique of EMG signal which replace the complicated mode of switching is the pattern recognition. This new control approach is stranded on the assumption that an EMG pattern contains information about the proposed movements involved in a residual limb. Using a technique of pattern classification, a variety of different intended movements can be identified by distinguishing characteristics of EMG patterns. Once a pattern has been classified, the movement is implemented through the command sent to a prosthesis controller. EMG pattern-recognition-based prosthetic control method involves performing EMG measurement (to capture reliable and consistent myoelectric signals), feature extraction (to recollect the most important discriminating information from the EMG), classification (to predict one of a subset of intentional movements), and multifunctional prosthesis control (to implement the operation of prosthesis by the predicted class of movement) [30]. EMG pattern recognition block diagram of Trans radial prosthesis shown in **Figure 5**.

In pattern recognition control for a multifunctional prosthesis, multi-channel myoelectric recordings are needed to capture enough myoelectric pattern information. The number and placement of electrodes would mainly depend on how many classes of movements are demanded in a multi-functional prosthesis and how many residual muscles of an amputee are applicable for myoelectric control. For myoelectric transradial prostheses, the EMG signals are measured from residual muscles with a number of bipolar electrodes (8-16) which are placed on the circumference of the remaining forearm in which 8 of the 12 electrodes were uniformly placed around the proximal portion of the forearm and the other 4 electrodes were positioned on the distal end. A large circular electrode was placed on the elbow of the amputated arm as a ground [31].

For acquisition of EMG signal 50 Hz-60 Hz can be used to remove or reduce more low-frequency to increase the control stability of a multifunctional myoelectric prosthesis [32]. EMG feature extraction is performed on windowed EMG data, all EMG recordings channels are segmented into a series of analysis windows either with or without time overlap (WL (window length) is 100-250 ms) shown in **Figure 6** [33].

*Service Robotics*

**Figure 4.** *AI and its functions.*

network model. Prediction is a method to predict a pattern an output noise free data

Implementation of artificial intelligence in controlling prostheses has increased drastically and thus enables the amputee to operate the prosthesis more desirably. Adaptive controlling would enable a system to perform closer to the desired output

The artificial Intelligence in upper extremity prosthesis used as direct control and indirect control from the neural network by various signal, sensor, controller and algorithm. The control signals are coming from the human in the two form for operation of upper extremity prosthesis i.e. electromyography (EMG) and Electroencephalogram (EEG). Prior attempts at voluntary control of the elements of prosthesis have focused on the use of electromyography (EMG) signals from muscle groups that remain under voluntary control. Most of this work has centered on control systems for upper extremity prostheses. The first commercialized powered hand myoelectric prosthesis was introduced by USSR in 1960 [27]. The advancement in EMG control myoelectric prosthesis was with use of EMG pattern recognition based control strategy [28]. This approach allows the user

by adjusting the input with the help of a feedback system. Recently, a mindcontrolled limb (type of myoelectric controlling) was introduced as the latest advancement in the artificial intelligence-aided control system. A joint project between the Pentagon and Johns Hopkins Applied Physics Laboratory (APL) has come up with a modular prosthetic limb which would be fully controlled by sensors implanted in the brain, and would even restore the sense of touch by sending electrical impulses from the limb back to the sensory cortex [25]. Chang et al. (2009) proposed a multilayer artificial neural network (ANN)-based model to discover the essential correlation between the intrinsic impaired neuromuscular activities of people with spina bifida (SB) and their extrinsic gait behaviors [26]. The application of AI in prosthetics and orthotics is divided into various subparts according to the involvement of the region that get affected i.e. Lower extremity prosthesis and Orthosis, Upper extremity Orthosis and prosthesis, and rehabilita-

Examples: EMG CNN based prosthetic hand, EGG based Mind controlled

prosthesis with sensory feedback, robotic arm, exoskeleton Orthosis.

with a model from input data in hidden layer.

tion aids like motorized mobility devices.

**4.1 AI in upper extremity prosthesis and orthosis**

**4. Application of AI in prosthetics and orthotics**

**24**

Overlapping analysis windows are used to maximally utilize the continuous stream of data and to produce a decision stream, for analysis, the duration of the overlapping (e.g., 50 ms) due to data buffering is the operational delay in real-time control and 50% of overlapping is suitable for the real time embedded system. The features are categorized as time domain (TD), frequency domain (FD) and time- frequency domain (TFD). The EMG features are extracted from each analysis window as a representation of EMG signal pattern. A feature set is extracted for each analysis window and all the recording channels, producing an *L*-dimensional feature vector. After computing the feature sets of all the channels, the entire EMG

**Figure 5.** *Process of EMG pattern recognition control.*

#### **Figure 6.**

*Windowing techniques, time to process each window analysis is 't' and decisions (d1, d2, d3). In adjacent windows the processing time is less and the classifier is idle most of the time but in overlapping windows increase frequency of class decision because the analysis window slides with small increment (inc), the amount of overlap is equal to processing time which help the controller to process next class decision before the previous decission has been completed [33].*

**27**

**Figure 7.**

*extraction and the start point is shifted every S.*

*Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation*

this situation (L × C × W = 44 × 4 × 3 i.e. L = 44, C = 4, W = 3).

feature matrix (L × C × W, where *L*, *C*, and *W* are the number of features, the number of channels, and the number of analysis windows, respectively) from the training set is provided to a classifier for training shown in **Figure 7**. Example: The features extracted from four channels of surface EMG in each window is 44 and the data analyzed for the three windowed length, the EMG feature matrix for

The aim of pattern recognition based classifier is to discriminate the intended movements from the EMG recordings as accurately as possible. Many classification techniques have been investigated, including linear discriminate analysis, Bayesian statistical methods, artificial neural networks, and fuzzy logic [34, 35]. The LDA classifier is much simpler to implement and much faster to train without compromising the accuracy (>93%). Then the performance of a trained classifier in identifying a movement is evaluated using the testing data set and measured by the

Number correctly classified samples 100%

The classification accuracies in identifying all the classes of movements are averaged to calculate the overall classification accuracy for a subject uses convolutional neural network (CNN). Block diagram for classification and regression

EMG pattern recognition based prosthesis control strategy is not suitable for people with shoulder disarticulation amputations because few muscles remain in their residual arm from which to extract myoelectric control signals. To address this challenge, a new neural machine interfacing (NMI) technology called targeted muscle reinnervation (TMR) have been proposed and developed at Rehabilitation Institute of Chicago (RIC), which has the ability to improve control performance of

TMR uses the remaining nerves from an amputated limb and transfers them onto substitute muscle groups that are not biomechanically functional because they are no longer attached to the missing arm. During this transfer procedure, target muscles are denervated so that they can be reinnervated by the residual arm nerves

*EMG windowing in continuous feature extraction. Size of successive window for analysis is L, the sEMG data for classification is divided into C segments for every L that is the length of integrated samples as a feature* 

multifunctional myoelectric upper-limb prostheses shown in **Figure 9** [37].

Total number of testing samples ´ (4)

*DOI: http://dx.doi.org/10.5772/intechopen.93903*

classification accuracy, which is defined as:

pattern shown in the **Figure 8** [36].

feature matrix (L × C × W, where *L*, *C*, and *W* are the number of features, the number of channels, and the number of analysis windows, respectively) from the training set is provided to a classifier for training shown in **Figure 7**. Example: The features extracted from four channels of surface EMG in each window is 44 and the data analyzed for the three windowed length, the EMG feature matrix for this situation (L × C × W = 44 × 4 × 3 i.e. L = 44, C = 4, W = 3).

The aim of pattern recognition based classifier is to discriminate the intended movements from the EMG recordings as accurately as possible. Many classification techniques have been investigated, including linear discriminate analysis, Bayesian statistical methods, artificial neural networks, and fuzzy logic [34, 35]. The LDA classifier is much simpler to implement and much faster to train without compromising the accuracy (>93%). Then the performance of a trained classifier in identifying a movement is evaluated using the testing data set and measured by the classification accuracy, which is defined as:

$$\frac{\text{Number correctly classified samples}}{\text{Total number of testing samples}} \times 100\% \tag{4}$$

The classification accuracies in identifying all the classes of movements are averaged to calculate the overall classification accuracy for a subject uses convolutional neural network (CNN). Block diagram for classification and regression pattern shown in the **Figure 8** [36].

EMG pattern recognition based prosthesis control strategy is not suitable for people with shoulder disarticulation amputations because few muscles remain in their residual arm from which to extract myoelectric control signals. To address this challenge, a new neural machine interfacing (NMI) technology called targeted muscle reinnervation (TMR) have been proposed and developed at Rehabilitation Institute of Chicago (RIC), which has the ability to improve control performance of multifunctional myoelectric upper-limb prostheses shown in **Figure 9** [37].

TMR uses the remaining nerves from an amputated limb and transfers them onto substitute muscle groups that are not biomechanically functional because they are no longer attached to the missing arm. During this transfer procedure, target muscles are denervated so that they can be reinnervated by the residual arm nerves

#### **Figure 7.**

*Service Robotics*

Overlapping analysis windows are used to maximally utilize the continuous stream of data and to produce a decision stream, for analysis, the duration of the overlapping (e.g., 50 ms) due to data buffering is the operational delay in real-time control and 50% of overlapping is suitable for the real time embedded system. The features are categorized as time domain (TD), frequency domain (FD) and time- frequency domain (TFD). The EMG features are extracted from each analysis window as a representation of EMG signal pattern. A feature set is extracted for each analysis window and all the recording channels, producing an *L*-dimensional feature vector. After computing the feature sets of all the channels, the entire EMG

*Windowing techniques, time to process each window analysis is 't' and decisions (d1, d2, d3). In adjacent windows the processing time is less and the classifier is idle most of the time but in overlapping windows increase frequency of class decision because the analysis window slides with small increment (inc), the amount of overlap is equal to processing time which help the controller to process next class decision before the previous* 

**26**

**Figure 6.**

*decission has been completed [33].*

**Figure 5.**

*Process of EMG pattern recognition control.*

*EMG windowing in continuous feature extraction. Size of successive window for analysis is L, the sEMG data for classification is divided into C segments for every L that is the length of integrated samples as a feature extraction and the start point is shifted every S.*

#### **Figure 8.**

*a. Pattern recognition is able to classify different movement patterns, but only in sequence, which limits multifunctional control. b. Regression control is able to identify different movements at the same time, leading to more intuitive prosthetic control [36].*

#### **Figure 9.**

that previously traveled to the arm prior to amputation. The reinnervated muscles then assist as biological amplifiers of the amputated nerve motor commands. During the surgery subcutaneous tissue is removed that, surface EMG signals are optimized for power and focal recording.

Another advanced technique to control the multifunctional limb is Virtual reality (VR) based platforms have been developed for the purposes of development and performance quantification of multifunctional myoelectric prosthesis control system These VR platforms are designed to create an efficient, flexible, and userfriendly environment for prosthetic control algorithm development in the laboratory, application in a clinical setting, and eventual use in an embedded system. The major function modules of this platform include multi-electrode EMG recording (up to 16 channels), classifier training and testing in offline, virtual and physical prosthesis control in real time to regulate performance shown in **Figure 10** [38].

Apart from EMG signal the Electroencephalography (EEG) is the widely used non-invasive method by placing the electrode on the scalp for picking brain signal that has been utilized in brain machine interface (BCI/BMI) applications. It has high temporal resolution (about 1 ms) in comparison with other brainwave measurements such as electrocorticograms (ECoGs), magneto encephalograms

**29**

**Figure 11.**

*Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation*

(MEGs), functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (fNIRS). The advanced prostheses may best control by EEG signal with BCI, connected by ANN. The neural signals associated with arm movements as control signals of artificial neuroprosthesis collected from either the cortex of brain directly or from residual nerves. The diagram of EEG based control and EMG pattern recognition based control in utilized in upper extremity prosthesis is shown

*Virtual reality system (VR), subjects can operate a simulated prosthetic arm to interact with virtual objects. Multiple input modalities such as motion tracking systems and EMG/EEG electrodes provide maximum flexibility when evaluating different control approaches. Figure shows a subject operating a prosthetic arm prototype in VR (right side). Subject controls the arm via real-time motion tracking (left side), and 3-D visual* 

*feedback is provided via stereoscopic goggles for closed loop operation [38].*

*Brain computer Interface (BCI), controlling prosthetic and orthotics devices.*

*DOI: http://dx.doi.org/10.5772/intechopen.93903*

in schematic **Figure 11**.

**Figure 10.**

*Targeted muscle reinnervation (TMR) [37].*

## *Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation DOI: http://dx.doi.org/10.5772/intechopen.93903*

(MEGs), functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (fNIRS). The advanced prostheses may best control by EEG signal with BCI, connected by ANN. The neural signals associated with arm movements as control signals of artificial neuroprosthesis collected from either the cortex of brain directly or from residual nerves. The diagram of EEG based control and EMG pattern recognition based control in utilized in upper extremity prosthesis is shown in schematic **Figure 11**.

#### **Figure 10.**

*Service Robotics*

**Figure 8.**

**Figure 9.**

*to more intuitive prosthetic control [36].*

**28**

that previously traveled to the arm prior to amputation. The reinnervated muscles then assist as biological amplifiers of the amputated nerve motor commands. During the surgery subcutaneous tissue is removed that, surface EMG signals are

*a. Pattern recognition is able to classify different movement patterns, but only in sequence, which limits multifunctional control. b. Regression control is able to identify different movements at the same time, leading* 

Another advanced technique to control the multifunctional limb is Virtual reality (VR) based platforms have been developed for the purposes of development and performance quantification of multifunctional myoelectric prosthesis control system These VR platforms are designed to create an efficient, flexible, and userfriendly environment for prosthetic control algorithm development in the laboratory, application in a clinical setting, and eventual use in an embedded system. The major function modules of this platform include multi-electrode EMG recording (up to 16 channels), classifier training and testing in offline, virtual and physical prosthesis control in real time to regulate performance shown in **Figure 10** [38]. Apart from EMG signal the Electroencephalography (EEG) is the widely used non-invasive method by placing the electrode on the scalp for picking brain signal that has been utilized in brain machine interface (BCI/BMI) applications. It has high temporal resolution (about 1 ms) in comparison with other brainwave measurements such as electrocorticograms (ECoGs), magneto encephalograms

optimized for power and focal recording.

*Targeted muscle reinnervation (TMR) [37].*

*Virtual reality system (VR), subjects can operate a simulated prosthetic arm to interact with virtual objects. Multiple input modalities such as motion tracking systems and EMG/EEG electrodes provide maximum flexibility when evaluating different control approaches. Figure shows a subject operating a prosthetic arm prototype in VR (right side). Subject controls the arm via real-time motion tracking (left side), and 3-D visual feedback is provided via stereoscopic goggles for closed loop operation [38].*

#### **Figure 11.** *Brain computer Interface (BCI), controlling prosthetic and orthotics devices.*

Examples: Ottobock Dynamic Arm Plus is a combination of Myo Hand Vari Plus Speed terminal device and Wrist rotator with custom TMR socket which control the six DOF [39]. Mind or thought controlled prosthesis uses EEG signal and ANN.

### *4.1.1 Recent advancement in control strategy in upper extremity prosthetics and orthotics*

Jafarzadeh M (2019) uses the novel deep convolutional neural network (6 convolutional layers and 2 deep layers) and FIFO memory for operation of prosthetic hand in real time. The novel CNN was implemented in Python 3.5 using tensor flow library [40].

Chih-Wei-Chen et al. (2009) developed BCI based hand Orthosis used cursor control interface with a simple LDA classifier, that classify the EEG signals to control the hand orthosis in to three state right, left and nil and the corresponding command as +1, −1 and 0. The four states of activities like grasp, open, holding and standby can control by these three commands. The +1 and − 1 command signifies grasp and open, command '0' is for standby mode depending on the feedback signals which are grasping force (F) and angular position (Ꝋ) collected from FSR and encoder [41].

#### **4.2 AI in lower extremity prosthesis and orthosis**

The first Artificial intelligence method used in the lower extremity as Intelligence prosthesis which is a knee joint that replace the hydraulic mechanism by combination of microprocessor controlled and hydraulic or pneumatic actuator.

The microprocessor as name suggests process the signal received by the first sensor known as knee angle sensor provides information about the knee's angle of flexion and extension and velocity of lateral and angular movement, unlike the human body, the sensor determines the direction of movement because of a magnetic implant and second sensor gathers information about weight placement.

Microprocessor receives the data or signals by the motion employed by the amputee and that data are analyzed and interpret to get the closer approximation to natural gait. This data provides information to the microprocessor about the device's position and the extent of its motion, which are essentially proprioceptive sensations. The data are stored in the memory of the microprocessor for the future use like a recurrent neural network (RNN). A series of wire networks which are similar in function to the body's nervous system. That is, it enables the sensors, microprocessor, servo motors, and hydraulic cylinder to communicate with each other. These networks connect the two sensors to the microprocessor, which transmits sensory data much like the ascending sensory pathways send information to the brain. The wires exiting the microprocessor leading to the servo motors carry "motion commands," mimicking the descending motor pathways which instruct muscles to contract and produce a desired movement.

As in the human nervous system, these wires are dedicated to specific communication circuits between the sensors, microprocessor, servo motors, and hydraulics. This computed data are used to control the resistance generated by the hydraulic cylinders through the small valve passes into and out of the cylinder which regulate extension and flexion of the knee joint in different sub phases of gait cycle. It controls knee joint motion from 0° to maximum 60-700 . This mechanism helps the amputee to do various activities like stair climbing, jogging, running and walking in uneven terrain.

The microprocessor knee joint uses various algorithms to achieve gait symmetry, motion analysis, stumble control and comfort. These algorithm are control

**31**

**Figure 13.**

*swing phase [44].*

**Figure 12.**

*Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation*

logic, Intent detection algorithm, Genetic algorithm, Fuzzy logic based classifier, Expectation maximization algorithm and Impedance control algorithm [42, 43]. The operation principle of a smart leg or intelligent prosthesis is shown in block diagram

The Prosthetic knee joints uses this microprocessor control mechanism with machine learning Artificial Intelligence are Otto Bock's C leg (1997), OssurRheo knee (2005), Power knee by Ossur (2006), Self-learning knee by DAW Industries, Plie knee from freedom Innovation, Intelligent Prosthesis (IP) (Blatchford, United Kingdom), Linx (Endolite, Blatchford Inc. United Kingdom), Orion 2 (Endolite, Blatchford Inc. United Kingdom), X2 prostheses (Otto Bock Orthopedic Industry, Minneapolis, MN), X3 prostheses (Otto Bock Orthopedic

The volitional EMG control robotics Transtibial prosthesis was developed in 2014 by Baojun Chen et al., which adapt the amputee to walk on slope with different angles. The combination of myoelectric and intrinsic controller reduces the fatigue of muscle and attention during walking [44]. The prototype design of prosthesis

*(a) Schematic diagram of prosthesis control by integrating the proposed myoelectric controller with the intrinsic controller. (b) Strategy of extracting amputee users' movement intention with a 200-ms window in* 

and schematic diagram of this mechanism showed in **Figure 13**.

*DOI: http://dx.doi.org/10.5772/intechopen.93903*

Industry, Minneapolis, MN) etc.

*Block diagram of controller based intelligent prosthesis.*

(**Figure 12**).

*Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation DOI: http://dx.doi.org/10.5772/intechopen.93903*

logic, Intent detection algorithm, Genetic algorithm, Fuzzy logic based classifier, Expectation maximization algorithm and Impedance control algorithm [42, 43]. The operation principle of a smart leg or intelligent prosthesis is shown in block diagram (**Figure 12**).

The Prosthetic knee joints uses this microprocessor control mechanism with machine learning Artificial Intelligence are Otto Bock's C leg (1997), OssurRheo knee (2005), Power knee by Ossur (2006), Self-learning knee by DAW Industries, Plie knee from freedom Innovation, Intelligent Prosthesis (IP) (Blatchford, United Kingdom), Linx (Endolite, Blatchford Inc. United Kingdom), Orion 2 (Endolite, Blatchford Inc. United Kingdom), X2 prostheses (Otto Bock Orthopedic Industry, Minneapolis, MN), X3 prostheses (Otto Bock Orthopedic Industry, Minneapolis, MN) etc.

The volitional EMG control robotics Transtibial prosthesis was developed in 2014 by Baojun Chen et al., which adapt the amputee to walk on slope with different angles. The combination of myoelectric and intrinsic controller reduces the fatigue of muscle and attention during walking [44]. The prototype design of prosthesis and schematic diagram of this mechanism showed in **Figure 13**.

**Figure 12.**

*Service Robotics*

*orthotics*

from FSR and encoder [41].

**4.2 AI in lower extremity prosthesis and orthosis**

muscles to contract and produce a desired movement.

motion from 0° to maximum 60-700

library [40].

Examples: Ottobock Dynamic Arm Plus is a combination of Myo Hand Vari Plus Speed terminal device and Wrist rotator with custom TMR socket which control the six DOF [39]. Mind or thought controlled prosthesis uses EEG signal and ANN.

Jafarzadeh M (2019) uses the novel deep convolutional neural network (6 convolutional layers and 2 deep layers) and FIFO memory for operation of prosthetic hand in real time. The novel CNN was implemented in Python 3.5 using tensor flow

Chih-Wei-Chen et al. (2009) developed BCI based hand Orthosis used cursor control interface with a simple LDA classifier, that classify the EEG signals to control the hand orthosis in to three state right, left and nil and the corresponding command as +1, −1 and 0. The four states of activities like grasp, open, holding and standby can control by these three commands. The +1 and − 1 command signifies grasp and open, command '0' is for standby mode depending on the feedback signals which are grasping force (F) and angular position (Ꝋ) collected

*4.1.1 Recent advancement in control strategy in upper extremity prosthetics and* 

The first Artificial intelligence method used in the lower extremity as

implant and second sensor gathers information about weight placement.

Intelligence prosthesis which is a knee joint that replace the hydraulic mechanism by combination of microprocessor controlled and hydraulic or pneumatic actuator. The microprocessor as name suggests process the signal received by the first sensor known as knee angle sensor provides information about the knee's angle of flexion and extension and velocity of lateral and angular movement, unlike the human body, the sensor determines the direction of movement because of a magnetic

Microprocessor receives the data or signals by the motion employed by the amputee and that data are analyzed and interpret to get the closer approximation to natural gait. This data provides information to the microprocessor about the device's position and the extent of its motion, which are essentially proprioceptive sensations. The data are stored in the memory of the microprocessor for the future use like a recurrent neural network (RNN). A series of wire networks which are similar in function to the body's nervous system. That is, it enables the sensors, microprocessor, servo motors, and hydraulic cylinder to communicate with each other. These networks connect the two sensors to the microprocessor, which transmits sensory data much like the ascending sensory pathways send information to the brain. The wires exiting the microprocessor leading to the servo motors carry "motion commands," mimicking the descending motor pathways which instruct

As in the human nervous system, these wires are dedicated to specific communication circuits between the sensors, microprocessor, servo motors, and hydraulics. This computed data are used to control the resistance generated by the hydraulic cylinders through the small valve passes into and out of the cylinder which regulate extension and flexion of the knee joint in different sub phases of gait cycle. It controls knee joint

activities like stair climbing, jogging, running and walking in uneven terrain. The microprocessor knee joint uses various algorithms to achieve gait symmetry, motion analysis, stumble control and comfort. These algorithm are control

. This mechanism helps the amputee to do various

**30**

*Block diagram of controller based intelligent prosthesis.*

#### **Figure 13.**

*(a) Schematic diagram of prosthesis control by integrating the proposed myoelectric controller with the intrinsic controller. (b) Strategy of extracting amputee users' movement intention with a 200-ms window in swing phase [44].*

To mimic the normal foot and ankle motion several prosthetic feet uses AI mechanism are élan Foot (Blatchford, United Kingdom), iPED (developed by Martin Bionics LLC and licensed to College Park Industries), Proprio Foot (Össur, Iceland), Power Foot BiOM (developed at MIT and licensed to iWalk) and Meridium foot (Ottobock) etc. These feet are integrated with foot and ankle sensor to sense the terrain, angle and force required in different phases to mimic the normal foot.

Apart from EMG Control lower extremity prosthesis can be controlled by EEG signal using BCI, the example of EEG based control prosthesis is BiOM.

Lower Extremity Orthosis is a supportive device to the patients those have lost their function due to traumatic, neurologic and congenital abnormalities. The working principle of the Orthosis for the patient like hemiplegia, paraplegia and traumatic brain injury is changed vigorously with the implementation of artificial intelligence like functional electrical stimulation, Brain computer Interface and myoelectric controller. The concept of machine learning implemented in some sensor embedded stance control Orthosis which help the paraplegic to achieve near to normal gait with some limitations. The concept of functional electrical stimulation (FES) started in the year of 1960. This is used in case of damage of brain or spinal cord, stroke, Multiple Sclerosis (MS) and cerebral palsy.

The Functional electrical stimulation (FES) is the application of electrical stimulus to a paralyzed nerve or muscle to restore or achieve function. FES is most often used in neuro rehabilitation and is routinely paired with task-specific practice. Neuroprosthesis is a common example in orthotic substitution [45]. Control system can be open loop or Feed forward control, closed-loop or Feed backward control and adaptive control can be applied to both Feed forward and Feed backward controller. In open-loop controlled FES, the electrical stimulator controls the output and closed-loop FES employs joint or muscle position sensors to facilitate greater responsiveness to muscle fatigue, or to irregularities in the environment [46].

Electrodes act as interfaces between the electrical stimulator and the nervous system. The FES utilizes electrical current to stimulate muscle contraction so that the paralyzed muscles can start functioning again. The desired purpose is to stimulate a motor response (muscle contraction) through activation of a specific group of nerve fibers, typically using fibers of peripheral nerves. This may be achieved by the activation of motor efferent nerve fibers showed in **Figure 14**. FES uses Adaptive

**33**

**5. Conclusion**

*Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation*

logic Network (ALN) and Inductive Learning Algorithm (IL) [47]. ALN is a type of artificial neural network for supervised learning which produces binary decision tree. This is a special type of feed forward multilayer perceptron the signal restricted to the Boolean logic. IL is a supervised learning produces decision tree in the form of

AI implemented Gait Orthosis for spinal cord injury patients are powered ankle foot Orthosis (PAFO) and Exoskeletons. PAFO is incorporated with EMG controller to control the activity of soleus muscle to perform the actions of plantar flexion and inhibit the artificial dorsiflexion. Exoskeletons are uses BCI or EMG controller to

Wheel chair and walking aids is the important gadget for the disable to perform daily activities and transfer. In this robotic world the smart wheel chairs and intelligent walking aid reduced the area of work limitation. Application of artificial neural network in state of art robotics and AI technologies in smart wheels enhances the quality of life with ease in performance. The smart wheeler robotic wheelchair was developed by using Inverse Reinforcement Learning (IRL) techniques which was able to achieve maximum safety and set of tasks easily as compared to joystick control wheel chair [51]. Visual joystick control intelligent wheel chair is most advanced wheelchair prototype control by "Hand Gesture' incorporate recurrent neural network (RNN) in joystick control makes it a smart joystick having driving flexibility to different kind of disability [52]. The schematic diagram of virtual

Smart cane is a boon for the visually impaired persons; it incorporated with raspberry PI 3 microcontroller, HC-SRC04 ultrasonic sensor for obstacle detection, WTV-SR IC recognition module for record and fix voice playback and GPS/GSM

Human being is the most intelligent and complex engineered structure created by almighty. It is really a tough challenge for the Prosthetist & Orthotist to replicate its lost anatomical structure and function. However with advancement in the field of AI and robotics has created a ray of hope for millions of persons with disabilities. The application of AI in the field of prosthetics and orthotics are in the initial stage and not so widely being practiced. Many projects using AI are in prototype Stage and not yet commercialized. High costs of these devices are being major limitations

simulation for visual joystick control showed in **Figure 15**.

module to save different locations [53].

*DOI: http://dx.doi.org/10.5772/intechopen.93903*

*Virtual simulation of visual joystick control wheelchair.*

IF, THEN, ELSE, etc. [48, 49].

**Figure 15.**

control the orthotic devices [50].

**4.3 AI in mobility devices**

**Figure 14.** *Controlled functional electrical stimulation [47].*

*Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation DOI: http://dx.doi.org/10.5772/intechopen.93903*

**Figure 15.**

*Service Robotics*

normal foot.

To mimic the normal foot and ankle motion several prosthetic feet uses AI mechanism are élan Foot (Blatchford, United Kingdom), iPED (developed by Martin Bionics LLC and licensed to College Park Industries), Proprio Foot (Össur, Iceland), Power Foot BiOM (developed at MIT and licensed to iWalk) and Meridium foot (Ottobock) etc. These feet are integrated with foot and ankle sensor to sense the terrain, angle and force required in different phases to mimic the

Apart from EMG Control lower extremity prosthesis can be controlled by EEG

Lower Extremity Orthosis is a supportive device to the patients those have lost their function due to traumatic, neurologic and congenital abnormalities. The working principle of the Orthosis for the patient like hemiplegia, paraplegia and traumatic brain injury is changed vigorously with the implementation of artificial intelligence like functional electrical stimulation, Brain computer Interface and myoelectric controller. The concept of machine learning implemented in some sensor embedded stance control Orthosis which help the paraplegic to achieve near to normal gait with some limitations. The concept of functional electrical stimulation (FES) started in the year of 1960. This is used in case of damage of brain or

The Functional electrical stimulation (FES) is the application of electrical stimulus to a paralyzed nerve or muscle to restore or achieve function. FES is most often used in neuro rehabilitation and is routinely paired with task-specific practice. Neuroprosthesis is a common example in orthotic substitution [45]. Control system can be open loop or Feed forward control, closed-loop or Feed backward control and adaptive control can be applied to both Feed forward and Feed backward controller. In open-loop controlled FES, the electrical stimulator controls the output and closed-loop FES employs joint or muscle position sensors to facilitate greater responsiveness to muscle fatigue, or to irregularities in the environment [46]. Electrodes act as interfaces between the electrical stimulator and the nervous system. The FES utilizes electrical current to stimulate muscle contraction so that the paralyzed muscles can start functioning again. The desired purpose is to stimulate a motor response (muscle contraction) through activation of a specific group of nerve fibers, typically using fibers of peripheral nerves. This may be achieved by the activation of motor efferent nerve fibers showed in **Figure 14**. FES uses Adaptive

signal using BCI, the example of EEG based control prosthesis is BiOM.

spinal cord, stroke, Multiple Sclerosis (MS) and cerebral palsy.

**32**

**Figure 14.**

*Controlled functional electrical stimulation [47].*

*Virtual simulation of visual joystick control wheelchair.*

logic Network (ALN) and Inductive Learning Algorithm (IL) [47]. ALN is a type of artificial neural network for supervised learning which produces binary decision tree. This is a special type of feed forward multilayer perceptron the signal restricted to the Boolean logic. IL is a supervised learning produces decision tree in the form of IF, THEN, ELSE, etc. [48, 49].

AI implemented Gait Orthosis for spinal cord injury patients are powered ankle foot Orthosis (PAFO) and Exoskeletons. PAFO is incorporated with EMG controller to control the activity of soleus muscle to perform the actions of plantar flexion and inhibit the artificial dorsiflexion. Exoskeletons are uses BCI or EMG controller to control the orthotic devices [50].

#### **4.3 AI in mobility devices**

Wheel chair and walking aids is the important gadget for the disable to perform daily activities and transfer. In this robotic world the smart wheel chairs and intelligent walking aid reduced the area of work limitation. Application of artificial neural network in state of art robotics and AI technologies in smart wheels enhances the quality of life with ease in performance. The smart wheeler robotic wheelchair was developed by using Inverse Reinforcement Learning (IRL) techniques which was able to achieve maximum safety and set of tasks easily as compared to joystick control wheel chair [51]. Visual joystick control intelligent wheel chair is most advanced wheelchair prototype control by "Hand Gesture' incorporate recurrent neural network (RNN) in joystick control makes it a smart joystick having driving flexibility to different kind of disability [52]. The schematic diagram of virtual simulation for visual joystick control showed in **Figure 15**.

Smart cane is a boon for the visually impaired persons; it incorporated with raspberry PI 3 microcontroller, HC-SRC04 ultrasonic sensor for obstacle detection, WTV-SR IC recognition module for record and fix voice playback and GPS/GSM module to save different locations [53].

## **5. Conclusion**

Human being is the most intelligent and complex engineered structure created by almighty. It is really a tough challenge for the Prosthetist & Orthotist to replicate its lost anatomical structure and function. However with advancement in the field of AI and robotics has created a ray of hope for millions of persons with disabilities. The application of AI in the field of prosthetics and orthotics are in the initial stage and not so widely being practiced. Many projects using AI are in prototype Stage and not yet commercialized. High costs of these devices are being major limitations

as many Persons with disabilities cannot afford it. Government bodies, manufacturing unit and funding agencies must come forward and invest in this field so that the highest quality and latest technology must reach to larger population of disabled in an affordable cost.
