Abstract
Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.
Highlights
In recent years, the demand for efficient gait event detection has been steadily increasing.Gait event identification can be used to control the on/off time of functional electrical stimulation devices for drop foot correction in stroke patients [1,2,3,4], operate an active orthotic device for ankle foot pathologies [5], assess rehabilitation effects in post-stroke patients with gait abnormality [6], Sensors 2016, 16, 1634; doi:10.3390/s16101634 www.mdpi.com/journal/sensorsSensors 2016, 16, 1634 and classify daily activity to aid exercise for health care in the elderly [7]
It was observed that the average F1 score for the force sensitive resistor (FSR) method across all recruited subjects were found to be 1.00 for both heel strike (HS) and toe off (TO) gait event detection on level ground terrain
Our results showed that average F1 scores of 0.99 and 0.98 for HS gait event detection were recorded, while the average F1 scores of 0.95 and 0.99 for TO gait event detection were obtained
Summary
The demand for efficient gait event detection has been steadily increasing.Gait event identification can be used to control the on/off time of functional electrical stimulation devices for drop foot correction in stroke patients [1,2,3,4], operate an active orthotic device for ankle foot pathologies [5], assess rehabilitation effects in post-stroke patients with gait abnormality [6], Sensors 2016, 16, 1634; doi:10.3390/s16101634 www.mdpi.com/journal/sensorsSensors 2016, 16, 1634 and classify daily activity to aid exercise for health care in the elderly [7]. The demand for efficient gait event detection has been steadily increasing. Gait event identification can be used to control the on/off time of functional electrical stimulation devices for drop foot correction in stroke patients [1,2,3,4], operate an active orthotic device for ankle foot pathologies [5], assess rehabilitation effects in post-stroke patients with gait abnormality [6], Sensors 2016, 16, 1634; doi:10.3390/s16101634 www.mdpi.com/journal/sensors. A major limitation of these systems is that they are expensive and restricted to a controlled laboratory environment [10]. To overcome these drawbacks, various wearable sensors have been developed for gait event detection [11]
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