A Practical Gait Feedback Method Based on Wearable Inertial Sensors for a Drop Foot Assistance Device

Abstract

To maximise the efficiency of gait interventions, gait phase and joint kinematics are important for closing the system loop of adaptive robotic control. However, few studies have applied an inertial sensor system including both gait phase detection and joint kinematic measurement. Many algorithms for joint measurement require careful alignment of the inertial measurement unit (IMU) to the body segment. In this paper, we propose a practical gait feedback method, which provides sufficient feedback without requiring precise alignment of the IMUs. The method incorporates a two-layer model to realise simultaneous gait stance and swing phase detection and ankle joint angle measurement. Recognition of gait phases is performed by a high-level probabilistic method using angular rate from the sensor attached to the shank while the ankle angle is calculated using a data fusion algorithm based on the complementary filter and sensor-to-segment calibration. The online performance of the algorithm was experimentally validated when 10 able-bodied participants walked on the treadmill with three different speeds. The outputs were compared to the ones measured by an optical motion analysis system. The results showed that the IMU-based algorithm achieved a good accuracy of the gait phase recognition (above 95%) with a short delay response below 20 ms and accurate angle measurements with root mean square errors below 3.5 • compared to the optical reference. It demonstrates that our method can be used to provide gait feedback for the correction of drop foot.

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

“…Soft-tissue artifacts have been found to be a source of noise in data processing [43], [66]. Some studies that used multiple IMUs were required to perform sensor-to-segment alignment and calibration [45], [92] to achieve better performance. This may increase the preparation time but is comparatively faster than using marker-based systems.…”

“…8) Integration of wearable sensor gait analysis and feedback system: In this review, several studies were found to analyze the data from wearable sensors and provide them as feedback to the user through various modalities such as audio, visual, and haptic feedback [54], [63], [92], [94]. Feedback strategies are needed for gait retraining or real-time assessment to correct certain parameters in rehabilitation or sports applications.…”

“…Real-time data streaming has become more common in recent years [46], [64], [65], [85], [86], [89], [94], [98], [103] and has contributed to improved processing time efficiency. Real-time data processing was implemented in [37], [54], [60], [82], [91], [92], [104], [107]; in [104], up to seven IMU units were used for lower limb joint angle estimation. We briefly discuss the studies that have implemented real-time data processing.…”

“…The validity of two commercial wearables, Stryd™ and RunScribe, for performing gait analysis was presented in [91], which were benchmarked against a high-speed video analysis recorded at 1000 Hz. The use of four IMUs attached to the shank and foot of both legs for gait phase recognition and ankle angle estimation in real time was studied in [92]. Another study [104] used seven IMUs placed near the center of mass, both thighs, shanks, and feet to estimate the joint angles in real time.…”

Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection

“…(multilayer) perceptron models 28,42,49,51,77 , as well as random forest classifiers 36,42,44 , K-nearest neighbours 42,54,78 , and other types of machine learning using, e.g., Bayesian models 31,32,73,76 , Gaussian mixture model 41 , and principal component analysis 39,40,51,73 . Echo state networks have the great advantage of low computational costs while still showing excellent performance.…”