Real-World Gait Bout Detection Using a Wrist Sensor: An Unsupervised Real-Life Validation

Abstract

Gait bouts (GB), as a prominent indication of physical activity, contain valuable fundamental information closely associated with human's health status. Therefore, objective assessment of the GB (e.g. detection, spatio-temporal analysis) during daily life is very important. A feasible and effective way of GB detection in real-world situations is using a wrist-mounted inertial measurement unit. However, the high degree of freedom of the wrist movements during daily-life situations imposes serious challenges for a precise and robust automatic detection. In this study, we deal with such challenges and propose an accurate algorithm to detect GB using a wrist-mounted accelerometer. Features, derived based on biomechanical criteria (intensity, periodicity, posture, and other non-gait dynamicity), along with a Bayes estimator followed by two physically-meaningful post-classification procedures are devised to optimize the performance. The proposed method has been validated against a shank-based reference algorithm on two datasets (29 young and 37 elderly healthy people). The method has achieved a high median [interquartile range] of 90.2 [80.4, 94.6] (%), 97.2 [95.8, 98.4] (%), 96.6 [94.4, 97.8] (%), 80.0 [65.1, 85.9] (%) and 82.6 [72.6, 88.5] (%) for the sensitivity, specificity, accuracy, precision, and F1-score of the detection of GB, respectively. Moreover, a high correlation ($R^{2}= 0.95$ ) was observed between the proposed method and the reference for the total duration of GB detected for each subject. The method has been also implemented in real time on a low power consumption prototype.

Keywords

• Real-world gait bout
• physical activity
• wrist accelerometer
• machine learning
• low power
• real-time