Classification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although surface electromyography (sEMG) signals have been combined with inputs from other sensors to detect walking intention, no study has yet classified walking environments using sEMG only. Therefore, the purpose of this study is to classify the current walking environment based on the entire sEMG profile gathered from selected muscles in the lower extremities. The muscle activations of selected muscles in the lower extremities were measured in 27 participants while they walked over flat-ground, upstairs, downstairs, uphill, and downhill. An artificial neural network (ANN) was employed to classify these walking environments using the entire sEMG profile recorded for all muscles during the stance phase. The result shows that the ANN was able to classify the current walking environment with high accuracy of 96.3% when using activation from all muscles. When muscle activation from flexor/extensor groups in the knee, ankle, and metatarsophalangeal joints were used individually to classify the environment, the triceps surae muscle activation showed the highest classification accuracy of 88.9%. In conclusion, a current walking environment was classified with high accuracy using an ANN based on only sEMG signals.

Although studies on terrain identification algorithms to control walking assistive devices have been conducted using sensor fusion, studies on transition classification using only electromyography (EMG) signals have yet to be conducted. Therefore, this study was to suggest an identification algorithm for transitions between walking environments based on the entire EMG signals of selected lower extremity muscles using a deep learning approach. The muscle activations of the rectus femoris, vastus medialis and lateralis, semitendinosus, biceps femoris, tibialis anterior, soleus, medial and lateral gastrocnemius, flexor hallucis longus, and extensor digitorum longus of 27 subjects were measured while walking on flat ground, upstairs, downstairs, uphill, and downhill and transitioning between these walking surfaces. An artificial neural network (ANN) was used to construct the model, taking the entire EMG profile during the stance phase as input, to identify transitions between walking environments. The results show that transitioning between walking environments, including continuously walking on a current terrain, was successfully classified with high accuracy of 95.4 % when using all muscle activations. When using a combination of muscle activations of the knee extensor, ankle extensor, and metatarsophalangeal flexor group as classifying parameters, the classification accuracy was 90.9 %. In conclusion, transitioning between gait environments could be identified with high accuracy with the ANN model using only EMG signals measured during the stance phase.

Background: Gait speed indicates morbidity and life expectancy in older adults, but the minimum walking distance for measurement remains unclear. This study aimed to determine the minimum distance required to measure clinically reliable gait speed using a smartphone camera and pose estimation, and whether the distance is influenced by subject characteristics or measurement methods. Methods: Twenty-four healthy volunteers (≥ 65 years old) performed a video-recorded 10-m gait test, including acceleration and deceleration. Fourteen body points were derived using a pose-estimation algorithm. Speed was calculated based on the center of mass or a leading foot which simulates a condition with a walkway sensor and validated against manual measurements. Multiple videos of gait over varying distances were obtained by cropping video frames at 0.1-m intervals. Variance in gait speed over specific distances was calculated using ANOVA. “Minimum required distance” was defined as the shortest distance where the confidence interval of gait speed did not exceed the minimal clinically important difference (0.1 m/sec). We also investigated which clinical, anthropometric, or epidemiological variables might influence it by assessing their association with gait speed variance using mean squared error from linear regression. Results: Gait speed measured by pose estimation (1.55 ± 0.18 m/s) showed a high level of agreement with manual measurement (1.56 ± 0.14 m/s), with an intraclass correlation coefficient of 0.889 (95% CI: 0.822–0.931). “Minimum required distance” was 2.1 m when gait speed was calculated with the center of mass with 95% confidence interval while “minimun required distance” based on a leading foot was 4.7 m with 90% confidence interval. Gait speed itself and muscle strength were positively correlated with gait speed variance (r = 0.250, p = 0.036 for gait speed; r = 0.312, p = 0.008 for knee extension strength; r = 0.230, p = 0.053 for grip strength), whereas other epidemiologic or clinical parameters, and physical performance scales, were not. Conclusions: Clinically reliable measurement of gait speed could be achieved over 2.1 m using a smartphone and pose estimation inducing the center of mass with 95% confidence interval. The weaker the muscle strength or the slower the gait speed, the shorter the distance might be required.

The metatarsophalangeal (MTP) joint is not considered in most current walking assistive devices even though it plays an important role during walking. The purpose of this study was to develop a new MTP assistive device and investigate its effectiveness on the muscle activities of the lower extremities during walking while wearing the device. The MTP assistive device is designed to support MTP flexion by transmitting force through a cable that runs parallel with the plantar fascia. Eight participants were instructed to walk at a constant speed on a treadmill while wearing the device. The muscle activities of their lower extremities and MTP joint kinematics were obtained during walking under both actuated and non-actuated conditions. Paired t-tests were performed to compare the differences in each dependent variable between the two conditions. The muscle activity of the MTP flexor was significantly reduced during walking under actuated conditions (p = 0.013), whereas no differences were found in the muscle activities of other muscles or in the MTP joint angle between actuated and non-actuated conditions (p > 0.05 for all comparisons). In conclusion, the cable-driven MTP assistive device is able to properly assist the MTP flexor without interfering with the action of other muscles in the lower extremities; as such, this MTP assistive device, when integrated into existing exoskeleton designs, has the potential to offer improved walking assistance by reducing the amount of muscle activity needed from the MTP flexor.

Objective: This study aimed to develop a deep learning-based volition-detection model to automate the diagnostic process and improve neuromuscular disease classification using needle electromyography (nEMG) signals. Methods: The model was developed using 376 nEMG signals from 57 subjects and independently evaluated on an external dataset of 751 nEMG signals from 115 subjects at a single tertiary medical center. The proposed model directly processed raw nEMG signals to automatically extract volition signals (motor unit action potentials), eliminating the need for physician-dependent preprocessing. Results: The optimal segment length for volition detection was 0.060 s, and nEMGNet demonstrated the best overall performance. Model-extracted volition signals yielded higher segment-wise classification performance than raw nEMG signals, with area under the receiver operating characteristic curve (AUROC) values of 0.806 for myopathy, 0.819 for neuropathy, and 0.819 for normal cases, compared with 0.761, 0.728, and 0.733, respectively. At the patient-wise level, AUROC improved by 20.10% for myopathy, 23.15% for neuropathy, and 23.15% for normal cases compared with physician-detected volition data. Conclusion: The proposed volition-detection model significantly enhances neuromuscular disease classification performance while enabling a fully automated and less labor-intensive diagnostic workflow.