Human Gait Activity Recognition Machine Learning Methods

Abstract

Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject’s quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm’s robustness was also verified with the successful detection of freezing gait episodes in a Parkinson’s disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

“…Where, 𝑝 𝑖 signifies the position vector for 𝑖𝑡ℎ follower, 𝑝 𝑗 denotes position vector for 𝑗𝑡ℎ follower, 𝑐 denotes the present iteration, 𝑐 𝑚𝑎𝑥 is maximum amount of repetitions allowed. 𝛼 and 𝛽 are random values generated within the range [1,2]. They also play a role in the algorithm's calculations.…”

“…In the area of lower limb prosthetics, the quest of seamlessly blending artificial limbs with the natural rhythm of human movement has fuelled a wave of innovation. The intricate dance between man and machine requires a profound understanding of user intent, and at the heart of this understanding lies the pivotal concept of activity recognition [1,2]. This paper delves into the transformative landscape of activity recognition in lower limb prosthetics, where technological strides intersect with the human experience, reshaping the possibilities for those seeking enhanced mobility [3].…”

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

“…Lastly, the model is further tested through an unseen test dataset. When the accuracy is acceptable and the process ends, the model returns to the training phase until it reaches the level of accuracy needed [69]. In gait application, three ML techniques are commonly used: supervised, unsupervised, and reinforcement learning.…”

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

“…Big clinical data requires data integration and analysis by connecting medical devices using an intelligent and distributed platform of sensors, effectors through (IoT) to local or cloud-based artificial intelligence/machine learning (AI/ML) systems. This approach allows the integration of data from different sources to provide earlier and more accurate diagnosis and prognosis of a patient's condition and take appropriate preventive and therapeutic actions [30][31][32] (Figures 4 and 5). Preventive medicine is based onsystems already in place that could change the face of medicine in the future, focusing on the prevention of disease and secondary changes and maintaining the best possible health (including the functional state of the user) instead of responding to a decline in health (functional state) in the form of therapeutic interven-tion [33].…”