Real-Time Gait Phase Detection Using Wearable Sensors for Transtibial Prosthesis Based on a kNN Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 11; p. 4242
• Main Authors: Rattanasak, Atcharawan, Uthansakul, Peerapong, Uthansakul, Monthippa, Jumphoo, Talit, Phapatanaburi, Khomdet, Sindhupakorn, Bura, Rooppakhun, Supakit
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.06.2022; MDPI
• Subjects: Accuracy; Algorithms; Classification; Design and construction; Gait; gait analysis; gait phase detection; Implants, Artificial; k-nearest neighbor; Machine learning; Neural networks; Prostheses; Prosthesis; Sensors; Support vector machines; transtibial prosthesis; Vision systems; Walking; Wearable computers; wearable sensor; Thailand; wearable sensor; k-nearest neighbor; gait phase detection; gait analysis; transtibial prosthesis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22114242

Those with disabilities who have lost their legs must use a prosthesis to walk. However, traditional prostheses have the disadvantage of being unable to move and support the human gait because there are no mechanisms or algorithms to control them. This makes it difficult for the wearer to walk. To overcome this problem, we developed an insole device with a wearable sensor for real-time gait phase detection based on the kNN (k-nearest neighbor) algorithm for prosthetic control. The kNN algorithm is used with the raw data obtained from the pressure sensors in the insole to predict seven walking phases, i.e., stand, heel strike, foot flat, midstance, heel off, toe-off, and swing. As a result, the predictive decision in each gait cycle to control the ankle movement of the transtibial prosthesis improves with each walk. The results in this study can provide 81.43% accuracy for gait phase detection, and can control the transtibial prosthetic effectively at the maximum walking speed of 6 km/h. Moreover, this insole device is small, lightweight and unaffected by the physical factors of the wearer.