OKANA HIROTA

Objective: This study aimed to examine the effect on maximum values of hand grip strength（ HGS） when HGS was measured twice alternately on the right and left hands,followed by a 5-minute rest period, and then measured again in the same method. Methods: The participants were 18 male collegiate students who did not routinely train for athletic performance, had no pain or disease in the upper limb, and had no limitation of joint motion distal to the shoulder joint. HGS was measured twice alternately on the right and left hands, and the mean （MEAN）, maximum （MAX）, and maximum mean（MM） values were calculated. Additionally, the contraction time （CT）, intertrial rest time （IRT）, and session time （ST） were measured. The first session （FS） was measured using the above method, followed by a 5-minute rest period, after which the second session（ SS） was measured using the same method as FS. Results: In SS, HGS showed a significant increase in MEAN, MAX, and MM values. In addition, CT, IRT, and ST were all significantly shortened in SS. Conclusion: The results of this study suggest that the onset of PAPE （post-activation performance enhancement） may have been a factor in the increase of HGS in SS. Furthermore, providing SS is considered to be one option for exerting higher muscle strength in HGS measurement.

This study presents a new method for evaluating the natural frequencies of bone-conducted sounds using spectrograms obtained by short-time Fourier transform (STFT) and machine learning (ML) and examines its validity for practical applications. The natural frequencies of bone-conducted sounds is conventionally evaluated using fast Fourier transform (FFT) analysis. Spectrograms obtained by STFT are used to visualize bone-conducted sounds as a two-dimensional time-frequency representation. Another advantage of spectrograms is that they facilitate the development of a convolutional neural network based ML model for predicting natural frequency. Forty-five healthy college students (20.36 ± 1.53 years) participated in this study, and five hammer impacts were applied to the right medial malleolus to record bone-conducted sounds generated at the right medial tibial condyle. Spectrograms were obtained from the recorded waveforms using STFT and input into a ML model trained with natural frequencies evaluated by FFT as a ground truth. The natural frequencies predicted by the ML model were compared with the natural frequencies evaluated by the FFT analysis. The mean absolute error was 12.92 ± 4.05 Hz, and the mean coefficient of determination (R²) was 0.845 ± 0.163, demonstrating the high evaluation accuracy of the proposed method. Feature analysis conducted using gradient-weighted class activation mapping revealed that the developed model focused on the peak sound pressure of the spectrogram when predicting the natural frequency. Further, it was found that the predicted values were large discrepancies from those evaluated by FFT analysis when the ML model failed to capture the peak sound pressure.

本研究の目的は、若年者が高齢者セット装着の有無による実験条件の差が、転倒時の各種パラメータにどのような影響を与えるかを明らかにすることであった。 実験では転倒時の頭部と腰部の加速度を測定した。頭部最大合成加速度では、男性の高齢者セット無よりも、高齢者セット有の方が有意に高かった。衝撃力では、男性の高齢者セット無よりも、高齢者セット有の方が有意に大きかった。本研究で実験対象とした大学生は、高齢者セットを装着していても、大腿骨近位部骨折の閾値となる衝撃力を受けていないことが明らかになった。

本論文は高齢者施設における実態調査と転倒実験について測定、検討した。高齢者施設おいて高齢者自身も筋力の衰えを感じ、転倒予防を普段から意識しており、職員は望ましい転倒予防策はないか？が課題として挙げられていることが明らかになった。 高齢者疑似体験セットを装着した大学生による転倒実験では、装着の有無による2条件で測定した。装着時の男性の頭部衝撃力が有意に大きいことが明らかになった。