**2. History of artificial intelligence (AI) in prosthetics and orthotics**

The first intelligent prosthesis developed by Chas. A. Blatchford & Sons, Ltd. in 1993 [8] and the improved version in 1995 named as Intelligent Prosthesis Plus [9] Blatchford in 1998 developed Adaptive prosthesis combining three actuation mechanisms of hydraulic, pneumatics and microprocessor. The fully microprocessor control knee developed in 1997 by Ottobock known as C-leg [10]. Rheo knee and power knee both developed by OSSUR in 2005 and 2006 subsequently uses onboard AI mechanism [11]. In late 2011 Ossur introduced the world first bionic leg with robotics mechanism known as "symbionic leg" and this time period the Genium X3 was lunched by Ottobock which allow backward walking and provide intuitive and natural motion during gait cycle [12]. On 2015 Blatchford group introduced Linx the world's first fully integrated limb has seven sensor and four CPU throughout the body of Leg. It allows coordination and synchronization of knee and ankle joint by sensing and analyzing data on user movement, activities, environment and terrain making standing up or walking on ramp more natural. The iwalk BiOM is the world first bionic foot with calf system commercially available from 2011 developed by Dr. Hugh Herr uses robotics mechanism to replicate the function of muscle and tendon with proprietary algorithm [13, 14]. The commercially available microprocessor control foot are Meridium (OttoBock, Germany), Elan (Blatchford, UK), Pro-prio (Össur, Iceland), Triton Smart Ankle (hereinafter referred as TSA) (Otto Bock, Germany), and Raize (Fil-lauer, USA) etc. available from 2011 in the market [15].

The first commercially available bionic hand lunched by Touch bionics in 2007 with individually powered digits and thumb has a choice of grip. The design again embedded with rotating thumb known as i- limb ultra and i- limb revolution designs implanted with Biosim and My i- limb app [16]. Bebionic was commercially available in the market in 2010 manufactured by RSL steeper and lunched by World congress, in 2017 it owned by Ottobock. Bebionic3 allows 14 different hold with two thumb position [17]. Michelangelo hand is the fully articulated robotic hand with electronically actuated thumb first fitted in the year 2010 developed by Ottobock [18]. The concept of brain

**19**

**Figure 1.**

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

enhance the function of amputated and paralyzed part of the body.

**3. Basic concept of AI and machine learning (ML)**

computer interface (BCI) implemented neuroprosthesis or mind control prosthesis which can able to recognize the real time data and a gadget to get nearly normal function is the demand of the day. The EEG based mind controlled smart prosthetic arm was presented in 2016 IEEE conference but till now this concept is not commercialized [19]. Researchers are on the path of developing more complex devices that mimic the natural brain by implementing artificial intelligence to on board computer that read and reply the nerve signal that transmitted to robotic prosthesis and Orthosis which

Machine learning contains elements of mathematics, statistics, and computer science, which is helping to drive advances in the development of artificial intelligence. It is the study of computer algorithms which expands and develops through experiences. This is a subset of AI as shown in **Figure 1**. The ML algorithm methods generally categorized two types supervised and unsupervised learning [20, 21].

The method of predicting a model on a trained range of inputs learning function to maps the known output, which discover the pattern of new sets of data. Example 1*:* To predict the model for microprocessor knee joint which is trained with numerous input or labeled data of the knee angle variation in different sub phase of gait cycle and apply on new amputee to predict the new data by the phase

Example 2*:* Intuitive myoelectric prosthesis or pattern recognition control

Pattern recognition is an automatically recognition of pattern applied in data analysis, signal processing etc. when the pattern of algorithm trained from labeled data that is supervised learning. When the model of algorithm is fruitfully trained, the model can be used for the prediction of a new data. The ultimate goal of this ML is to develop a successful predictor function. The models of discrete or categorical categories of dependent variables are known as classification algorithm and with continuous value known as regression algorithm. Three basic steps followed to finalize a model are training, validating and application of algorithm to new data. Algorithm used for supervised learning are support vector machines, linear regression, linear

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

**3.1 Machine learning**

*3.1.1 Supervised learning*

prosthesis, FES.

dependent pattern recognition approach.

discriminant analysis (LDA) etc. This is error based learning.

*Relationship between artificial intelligence (AI), machine learning (ML) and deep learning (DL).*

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

computer interface (BCI) implemented neuroprosthesis or mind control prosthesis which can able to recognize the real time data and a gadget to get nearly normal function is the demand of the day. The EEG based mind controlled smart prosthetic arm was presented in 2016 IEEE conference but till now this concept is not commercialized [19]. Researchers are on the path of developing more complex devices that mimic the natural brain by implementing artificial intelligence to on board computer that read and reply the nerve signal that transmitted to robotic prosthesis and Orthosis which enhance the function of amputated and paralyzed part of the body.
