Pattern identification of different human joints for different human walking styles using inertial measurement unit (IMU) sensor

• Published in: The Artificial intelligence review Vol. 55; no. 2; pp. 1149 - 1169
• Main Authors: Semwal, Vijay Bhaskar, Gaud, Neha, Lalwani, Praveen, Bijalwan, Vishwanath, Alok, Abhay Kumar
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2022; Springer; Springer Nature B.V
• Subjects: Accelerometers; Analysis; Artificial Intelligence; Artificial neural networks; Classification; Computer Science; Gait; Human performance; Humanoid; Inertial platforms; Inertial sensing devices; Machine learning; Measurement; Measuring instruments; Neural networks; Robots; Sensors; Thigh; Walking; Connectionist learning; Biometrics; Deep learning; Human activities recognition; Clinical gait; Motion analysis
• ISSN: 0269-2821; 1573-7462
• DOI: 10.1007/s10462-021-09979-x

A bipedal walking robot is a kind of humanoid robot. It is suppose to mimics human behavior and designed to perform human specific tasks. Currently, humanoid robots are not capable to walk like human being. To perform the walking task, in the current work, human gait data of six different walking styles named brisk walk, normal walk, very slow walk, medium walk, jogging and fast walk is collected through our configured IMU sensor and mobile-based accelerometers device. To capture the pattern for six different walking styles, data is extracted for hip, knee, ankle, shank, thigh and foot. A total six classes of walking activities are explored for clinical examination. The accelerometer is placed at center of the human body of 15 male and 10 female subjects. In the experimental setup, we have done exploratory analysis over the different gait capturing techniques, different gait features and different gait classification techniques. For the classification purpose, three state of art techniques are used as artificial neural network, extreme learning machine and deep neural network learning based CNN mode. The model classification accuracy is obtained as 87.4%, 88% and 92%, respectively. Here, WISDM activity data set is also used for verification purpose.