**3.1 MEMSense IMU**

*Gyroscopes - Principles and Applications*

dorsiflexion angles on the sagittal plane.

*Kneeless inverted pendulum model of walking gait.*

**3. Gait data acquisition**

**Figure 7.**

where *L* denotes the pendulum length related to the subject's height and leg length and θ1 and θ2 denote the amplitudes that the pendulum swings away from vertical, which are approximated by the maximum positive and negative rotation angles of foot pitch motion, respectively, and related to the plantarflexion and

Gait analysis can be achieved by examining the patterns of sensed data from the measuring instruments. There are two sources of gait data in our study, i.e., inertial data and optoelectronic data. The optoelectronic data are measured by using the Vicon® optical motion capture system from Oxford Metrics Ltd., UK [10], which is used as reference data to provide ground truth for gait analysis algorithms. As illustrated in **Figure 8(a)**, the MEMS inertial sensors and Vicon retroreflective markers are attached to the subject's lower limbs. However, for demonstration purpose, only the measurements from foot-mounted devices are considered in this chapter, as shown in the partial enlarged drawing in **Figure 8(b)**. Two types of inertial

*System setup for gait data acquisition. (a) Sensor placement on lower limbs and (b) Foot-mounted sensors and* 

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**Figure 8.**

*markers.*

The first type of inertial sensors used in our study is the MEMSense nIMU [13, 33], which is a small-size and low-weight MEMS unit and costs about \$1300, as shown in **Figure 9** that illustrates the data acquisition process under normal conditions. Since nIMU is a wired sensor node, when communicating with it, one needs to connect it to a USB interface board first and then connect the USB interface board to a computer for further processing. The software used for acquiring and storing sensed data is the MEMSense IMU Data Console (IDC), which is a console-based, menu-driven application and allows basic display and collection using a specified RS422 protocol.

The nIMU is compensated for temperature sensitivities to bias and scale factor and provides serial outputs including 3D acceleration, 3D angular rate, and 3D magnetic field intensity, with a sampling rate of 150 Hz. The key manufacturer specifications of the gyroscope in nIMU are listed in **Table 1**.

A segment of raw measurements is shown in **Figure 10**. As the IMUs are placed on the subject's feet, the gyroscope measurements feature periodic and repetitive patterns according to the transitions of gait phases. These patterns are helpful for gait analysis, by facilitating the detection of the key gait events and the concerned gait phases correspondingly. Since the feet are exposed to quite extreme dynamics at HS events, it is found that the bandwidth and dynamic ranges of the gyroscope in nIMU are insufficient for optimal gait characterization, as seen in **Figure 10**. These insufficiencies would induce systematic measurement and modeling errors to the system. When testing the sensor for running gait, the achieved tracking results are reasonable but would improve considerably if the gyroscope has sufficient dynamic range, so as to accurately monitor the impact of foot on the ground. According to research in [34], the maximum angular velocity experienced by toe-mounted gyroscopes can

**Figure 9.** *MEMSense nIMU used for gait data collection.*


**Table 1.**

*Key specifications of gyroscope in MEMSense nIMU.*

**Figure 10.**

*Raw gyroscope measurements of MEMSense nIMU. (a) Walking at 130 steps/min anf (b) Running at 170 steps/min.*

reach 1500°/s during running and 2000°/s during sprinting. This is because the foot attitude changes very rapidly over the gait cycle, especially for the toe motion that exhibits the highest angular velocity. The maximum angular velocities experienced by the heel, ankle, and shin are no higher than 1000°/s during running.

#### **3.2 Analog devices IMU**

The other type of inertial sensors used in our study is the ADIS16448 *i*Sensor® device [35–37], which combines industry-leading *i*MEMS® technology with signal conditioning that optimizes dynamic performance and costs about \$600. The ADIS16448 is packaged in a module that has a standard connector interface, as illustrated in **Figure 11** that depicts the data acquisition process in a physical therapy and rehabilitation department of a public hospital. The SPI and register structures provide a simple interface for data collection and configuration control. The ADIS16448 has a compatible pinout for systems that currently use other Analog Devices, Inc., IMU products. Each ADIS16448 includes a triaxial gyroscope, a triaxial accelerometer, a triaxial magnetometer, and pressure sensors. The factory calibration characterizes each sensor for sensitivity, bias, and alignment. Thus, each sensor has its own dynamic compensation formulas that provide accurate sensor measurements. The key manufacturer specifications of the gyroscope in ADIS16448 are listed in **Table 2**.

The dimensions of the entire sensing assembly are 4.5 × 3.5 × 2.25 cm, and the sampling rate is 400 Hz. The main components include the ADIS16448 IMU, a printed circuit assembly (PCA) with a microcontroller, a power supply, and a casing enclosing all of the components. The collected data were stored in internal memory first and then transferred to an external computer for further processing. A segment of raw measurements is shown in **Figure 12**. It can be seen that the gyroscope

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*Applications of MEMS Gyroscope for Human Gait Analysis*

Mass (g) 15 Size (mm) 24.1 × 37.7 × 10.8 Operating temperature (°C) −40 to +85 Gyroscope Range (°/s) ±1000

> Nonlinearity (% of FS) ±0.1 Noise (°/s) 0.27 Bandwidth (Hz) 330

range of ±1000°/s would be more suitable, as the sensor readings stay within this

*Raw gyroscope measurements of ADIS16448* i*Sensor® device. (a) Walking at 3 km/h and (b) Running at* 

In this chapter, the raw measurements of accelerometer and gyroscope are compared first, then the gait events are identified by using a rule-based method,

Different methods have been presented for gait detection in the literature [38]. In a sense, gait phases are a function of time and inertial measurements. A segment of raw measurements is shown in **Figure 13**, including specific forces and angular rates of both feet measured by the accelerometer and the gyroscope, respectively, together with the key gait events and their delimited gait phases. Gait detection can be achieved by using a rule-based method from the raw measurements or its magnitude [39], root mean square [40], and moving average [41], which is straightforward and easy to implement. Different detection methods have been compared in [42], and the results suggest that angular rate is more reliable than acceleration for typical walking. As can be seen in **Figure 13**, the angular rates provide more prominent characteristics than the specific forces for gait detection, especially the angular rate around *Z*-axis in the sagittal plane. Due to the specificity of foot motion, there

and finally the false-detected gait phases are discussed and eliminated.

dynamic range during walking and running at varying speeds.

are at least two possible explanations for this phenomenon:

**4. Rule-based gait detection**

**4.1 Raw inertial measurements**

*DOI: http://dx.doi.org/10.5772/intechopen.86837*

*Key specifications of gyroscope in ADIS16448* i*Sensor® device.*

**Table 2.**

**Figure 12.**

*6 km/h.*

**Figure 11.** *ADIS16448* i*Sensor® device used for gait data collection.*


**Table 2.**

*Gyroscopes - Principles and Applications*

**3.2 Analog devices IMU**

**Figure 10.**

ADIS16448 are listed in **Table 2**.

*ADIS16448* i*Sensor® device used for gait data collection.*

reach 1500°/s during running and 2000°/s during sprinting. This is because the foot attitude changes very rapidly over the gait cycle, especially for the toe motion that exhibits the highest angular velocity. The maximum angular velocities experienced

*Raw gyroscope measurements of MEMSense nIMU. (a) Walking at 130 steps/min anf (b) Running at 170 steps/min.*

The other type of inertial sensors used in our study is the ADIS16448 *i*Sensor® device [35–37], which combines industry-leading *i*MEMS® technology with signal conditioning that optimizes dynamic performance and costs about \$600. The ADIS16448 is packaged in a module that has a standard connector interface, as illustrated in **Figure 11** that depicts the data acquisition process in a physical therapy and rehabilitation department of a public hospital. The SPI and register structures provide a simple interface for data collection and configuration control. The ADIS16448 has a compatible pinout for systems that currently use other

Analog Devices, Inc., IMU products. Each ADIS16448 includes a triaxial gyroscope, a triaxial accelerometer, a triaxial magnetometer, and pressure sensors. The factory calibration characterizes each sensor for sensitivity, bias, and alignment. Thus, each sensor has its own dynamic compensation formulas that provide accurate sensor measurements. The key manufacturer specifications of the gyroscope in

The dimensions of the entire sensing assembly are 4.5 × 3.5 × 2.25 cm, and the sampling rate is 400 Hz. The main components include the ADIS16448 IMU, a printed circuit assembly (PCA) with a microcontroller, a power supply, and a casing enclosing all of the components. The collected data were stored in internal memory first and then transferred to an external computer for further processing. A segment of raw measurements is shown in **Figure 12**. It can be seen that the gyroscope

by the heel, ankle, and shin are no higher than 1000°/s during running.

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**Figure 11.**

*Key specifications of gyroscope in ADIS16448* i*Sensor® device.*

**Figure 12.**

*Raw gyroscope measurements of ADIS16448* i*Sensor® device. (a) Walking at 3 km/h and (b) Running at 6 km/h.*

range of ±1000°/s would be more suitable, as the sensor readings stay within this dynamic range during walking and running at varying speeds.
