BACKGROUND: Wearable technologies have been developed for healthy aging. The technology for electromyography (EMG)-controlled functional electrical stimulation (FES) systems has been developed, but research on how helpful it is in daily life has been insufficient. OBJECTIVE: The purpose of this study was to investigate the effect of the EMG-controlled FES system on muscle morphology, balance, and gait in older adults. METHODS: Twenty-nine older adults were evaluated under two randomly assigned conditions (non-FES and FES assists). Muscle morphology, balance, gait function, and muscle effort during gait were measured using ultrasonography, a physical test, a gait analysis system, and EMG. RESULTS: The EMG-controlled FES system improved gait speed by 11.1% and cadence by 15.6% (P< 0.01). The symmetry ratio of the bilateral gastrocnemius was improved by 9.9% in the stance phase and 11.8% in the swing phase (P< 0.05). The degrees of coactivation of the knee and ankle muscles were reduced by 45.1% and 50.5%, respectively (P< 0.05). Balance improved by 6–10.7% (P< 0.01). CONCLUSION: The EMG-controlled FES system is useful for balance and gait function by increasing muscle symmetry and decreasing muscle coactivation during walking in older adults.

We propose a novel dual-channel electromyography (EMG) spatio-temporal differential (DESTD) method that can estimate volitional electromyography (vEMG) signals during time-varying functional electrical stimulation (FES). The proposed method uses two pairs of EMG signals from the same stimulated muscle to calculate the spatio-temporal difference between the signals. We performed an experimental study with five healthy participants to evaluate the vEMG signal estimation performance of the DESTD method and compare it with that of the conventional comb filter and Gram–Schmidt methods. The normalized root mean square error (NRMSE) values between the semi-simulated raw vEMG signal and vEMG signals which were estimated using the DESTD method and conventional methods, and the two-tailed t-test and analysis of variance were conducted. The results showed that under the stimulation of the gastrocnemius muscle with rapid and dynamically modulated stimulation intensity, the DESTD method had a lower NRMSE compared to the conventional methods (p < 0.01) for all stimulation intensities (maximum 5, 10, 15, and 20 mA). We demonstrated that the DESTD method could be applied to wearable EMG-controlled FES systems because it estimated vEMG signals more effectively compared to the conventional methods under dynamic FES conditions and removed unnecessary FES-induced EMG signals.

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This study proposes a dependency grammar‐based self‐attention multilayered bidirectional long short‐term memory (DG‐M‐Bi‐LSTM) model for subject–predicate–object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high‐accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG‐M‐Bi‐LSTM is compared with that using NL‐based self‐attention multilayered bidirectional LSTM, DG‐based bidirectional encoder representations from transformers (BERT), and NL‐based BERT to evaluate its effectiveness. The DG‐M‐Bi‐LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.