Objective: Utilization of inertial measurement units (IMU) data for ground reaction force (GRF) prediction has been widely studied and documented when these sensors attach to the body segments. However, it was inconvenient and required people's cooperation. A novel approach of the current study was setting IMU sensors mounted underneath the walking surface to measure footstep induced structural vibration. We aimed to conduct the force plate to validate the prediction accuracy of this approach. Methods: Fifteen hundred steps were recorded from five individuals. Twentyfour measured features from four IMU sensors were treated as inputs to the long short-term memory model for multidimensional GRF predictions. The GRF data from the force plate were considered as the ground truth for comparisons. The accuracy performance was determined by the normalized root mean square error (NRMSE) method. Results: The averaged NRMSE was 6.05%, 3.93%, and 4.37% for Fx, Fy, and Fz, respectively. Conclusion: The accuracy was comparable with IMU sensors attached to the body, particularly in the vertical direction. The current study demonstrated the feasibility of this approach and successfully predicted ground reaction force with high accuracy. Significance: The validation of IMU sensors mounted underneath the walking surface for GRF prediction provides an alternative method for biometrics in gait.

Center of pressure (COP) during a gait cycle indicates crucial information with regard to fall risk such as balance capacity. The drawbacks of conventional research instruments include inconvenient use during activities of daily living and expensive costs. The present study illustrates the promising fall-relevant information predicted by acceleration and angular velocity data from different placement sensors with machine learning techniques. This approach is inspired by the emerging machine learning technique, specifically the long short-term memory (LSTM), which is often used in time series data and aims to decrease the burden of the user while using the novel wearable technology. The Jaccard similarity coefficient, which implies the consistency of profile alignment between prediction and real situation, achieved 94% accuracy in the walking direction. Furthermore, the number of sensors used and the placement influenced the feasibility of an application. The outcome revealed that the accuracy could exceed 90% with only one sensor placed on the foot in the walking direction, and the toe would be the best location for sensor placement. To examine the performance of machine learning, the current study employed two parameters from different perspectives. One is a commonly used parameter, which represented the error, and the other investigated the similarity between the prediction and ground truth. From a similarity perspective, the parameter can be used as a metric to assess the consistency of profile alignment.

Sound and sound frequency could improve postural sway in the elderly. The power spectrum intervals of the center of pressure (COP) displacement are associated with different postural regulations, which could be revealed by frequency analysis. The aim of the study was to investigate the effects of sound on dual-tasking postural control and conduct frequency analysis to distinguish postural regulations in the elderly. Fifteen young and 15 older healthy participants were instructed to stand on a force platform and performed the Purdue Pegboard test while hearing 50 dB sounds with sound frequencies of 250 Hz, 1000 Hz, 4000 Hz, or no sound. The total excursion, velocity, sway area, and power spectrum of low-, medium-, and high-frequency bands of the COP displacement were calculated in the anterior–posterior and medial–lateral directions. The percentages of low-frequency and medium-frequency bands in both directions were significantly different between with and without sound conditions, but not affected by sound frequency. Older adults showed a smaller percentage of low-frequency, larger percentage of medium-frequency, larger total COP excursion, and faster velocity in the medial–lateral direction. The outcome of the study supports the frequency analysis approach in evaluating sound effects on postural strategies in dual-tasking and reveals older adults utilize vestibular regulation as the primary postural strategy when the dual-task required visual attention.

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