Assistive motion for sit-to-stand causes lower back pain (LBP) among caregivers. Considering previous studies that showed that foot position adjustment could reduce lumbar load during assistive motion for sit-to-stand, quantitative monitoring of and instructions on foot position could contribute toward reducing LBP among caregivers. The present study proposes and evaluates a new method for the quantitative measurement of foot position during assistive motion for sit-to-stand using a few wearable sensors that are not limited to the measurement area. The proposed method measures quantitative foot position (anteroposterior and mediolateral distance between both feet) through a machine learning technique using features obtained from only a single inertial sensor on the trunk and shoe-type force sensors. During the experiment, the accuracy of the proposed method was investigated by comparing the obtained values with those from an optical motion capture system. The results showed that the proposed method produced only minor errors (less than 6.5% of body height) when measuring foot position during assistive motion for sit-to-stand. Furthermore, Bland–Altman plots suggested no fixed errors between the proposed method and the optical motion capture system. These results suggest that the proposed method could be utilized for measuring foot position during assistive motion for sit-to-stand.

Falls are among the main causes of injuries in elderly individuals. Balance and mobility impairment are major indicators of fall risk in this group. The objective of this research was to develop a fall risk feedback system that operates in real time using an inertial sensor-based instrumented cane. Based on inertial sensor data, the proposed system estimates the kinematics (contact phase and orientation) of the cane. First, the contact phase of the cane was estimated by a convolutional neural network. Next, various algorithms for the cane orientation estimation were compared and validated using an optical motion capture system. The proposed cane contact phase prediction model achieved higher accuracy than the previous models. In the cane orientation estimation, the Madgwick filter yielded the best results overall. Finally, the proposed system was able to estimate both the contact phase and orientation in real time in a single-board computer.

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It is estimated that one in three seniors fall at least once a year. Falls are a global problem for the elderly that affects their quality of life and poses a great risk. In our research, we are trying to develop a system that could prevent falls by estimating the fall risk in real time. The system would measure the balance of the user by measuring the position of the Center of Gravity inside the Base of Support. In our previous research, we presented a system with a millimeter wave radar attached to a cane to measure the area of the Base of Support. However, the obtained results for the foot position estimation error were significantly worse than similar studies. One of the reasons was that the sensor was not really estimating the position of the feet but the position of the lower legs. Therefore, in this research we present a correction model to improve the feet position estimation. The proposed model was able to reduce the foot position estimation RMSE from 54 mm down to 34 mm, which is closer to the results of other similar studies measuring the position of the feet.