Walking pattern classification using a granular linguistic analysis

An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection

Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation-while ignoring the inherent correlation in high-dimensional spaces-which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.

A Novel Accelerometer-Based Technique for Robust Detection of Walking Direction

OBJECTIVE

Distance estimation in pedestrian dead reckoning is acquired using vector norm of accelerations, which results in positive values. However, anteroposterior acceleration is negative when a step is taken backward, which must be detected for accurate localization. This paper proposes a novel approach for the detection of walking direction, which uses a dominant trend duration.

METHODS

The approach evaluates anteroposterior acceleration out of a foot-worn accelerometer for temporal dominance of acceleration trends during swing phase of the walk. The approach is tested for forward and backward walks with speed variations on a straight path as well as for forward walk at normal speed on a turning path. To validate the detection accuracy, success rates per participant per walk trial are calculated and then overall success rate for all the trials are reported. Moreover, metrics precision, recall and F1 scores are calculated for detection reliability in both directions.

RESULTS

Overall 98 ± 2% detection accuracy is achieved on linear path considering both directions and all speed variations, whereas 93 ± 7% on turning path including left and right turns. In comparison with the state-of-the-art bidirectional detection approach, the proposed approach delivers accurate detection with speed variations without requiring prior training and relies on a single sensory feature.

CONCLUSION

Dominant trend duration is a novel and reliable feature to detect directional changes during communal walk with speed variation.

SIGNIFICANCE

The approach can be employed in different contexts, such as enabling pedestrian localization approaches to accommodate back stepping or any application that requires knowledge of changing directions while walking.