Search

Sarcopenia is a comprehensive degenerative disease with the progressive loss of skeletal muscle mass with age, accompanied by the loss of muscle strength and muscle dysfunction. Individuals with unmanaged sarcopenia may experience adverse outcomes. Periodically monitoring muscle function to detect muscle degeneration caused by sarcopenia and treating degenerated muscles is essential. We proposed a digital biomarker measurement technique using surface electromyography (sEMG) with electrical stimulation and wearable device to conveniently monitor muscle function at home. When motor neurons and muscle fibers are electrically stimulated, stimulated muscle contraction signals (SMCSs) can be obtained using an sEMG sensor. As motor neuron activation is important for muscle contraction and strength, their action potentials for electrical stimulation represent the muscle function. Thus, the SMCSs are closely related to muscle function, presumptively. Using the SMCSs data, a feature vector concatenating spectrogram-based features and deep learning features extracted from a convolutional neural network model using continuous wavelet transform images was used as the input to train a regression model for measuring the digital biomarker. To verify muscle function measurement technique, we recruited 98 healthy participants aged 20–60 years including 48 [49%] men who volunteered for this study. The Pearson correlation coefficient between the label and model estimates was 0.89, suggesting that the proposed model can robustly estimate the label using SMCSs, with mean error and standard deviation of -0.06 and 0.68, respectively. In conclusion, measuring muscle function using the proposed system that involves SMCSs is feasible. [ABSTRACT FROM AUTHOR]

Aim: Physical frailty and sarcopenia are geriatric syndromes associated with adverse health outcomes. However, the common and different conditions associated with physical frailty and sarcopenia remain unclear. This study aimed to compare the clinical characteristics of older adults with physical frailty and sarcopenia. Methods: This cross‐sectional study analyzed 2028 community‐dwelling older adults, aged 70–84 years (mean age 75.9 ± 4.0 years; 50.1% men). Physical frailty was defined using the Fried frailty phenotype, whereas sarcopenia was defined using the Asian Working Group for Sarcopenia 2014 and 2019 criteria. Results: The prevalence of physical frailty was 5.5%, whereas that of sarcopenia, using the Asian Working Group for Sarcopenia 2014 and 2019, was 9.1% and 17.5%, respectively. The concordance of physical frailty with sarcopenia was higher in the Asian Working Group for Sarcopenia 2019 criteria than in 2014. Frail‐only participants had a higher incidence of disability, cognitive dysfunction, poor self‐perceived health, and depressive symptoms than the sarcopenic‐only participants. Frail‐and‐sarcopenic participants showed a lower body mass index (≥23 kg/m2) than frail‐only participants. Furthermore, in frail‐and‐sarcopenic participants, recurrent falls (16.9% vs 5.5%) were higher than in the neither‐frail‐nor‐sarcopenic participants; falls with fracture (11.5% vs 1.7%) and fear of falling (59.0% vs 36.7%) were higher in frail‐and‐sarcopenic participants than in sarcopenic‐only participants. Conclusions: This study showed significant differences between physical frailty and sarcopenia among community‐dwelling older adults. Older adults with physical frailty and sarcopenia were more likely to have higher fall‐related outcomes than the others. Geriatr Gerontol Int 2022; 22: 42–49. [ABSTRACT FROM AUTHOR]

This paper presents a 10-channel, 120 nW/channel, reconfigurable capacitance-to-digital converter (CDC) enabling sub- μW wearable sensing applications. The proposed multi-channel architecture supports 10 channels with a shared reconfigurable 6-bit differential analog-to-digital converter (ADC). The reconfigurable nature of the CDC enables adaptive sensing range and sensing speed based on the target application. Furthermore, the architecture performs both on/off-chip parasitic correction and baseline calibration to measure the change in capacitance ( ∆C), excluding baseline and parasitic capacitances. The experimental results show the measurement range of ∆C are 5.34 pF for 1x sensitivity and 1.8 pF for 3x sensitivity respectively. The capacitive divider-based architecture excludes power-hungry operational trans-impedance amplifiers for capacitance to voltage conversion, and the architecture supports programmable channel access to activate or deactivate each channel independently. The random interrupt protection logic avoids any broken sample or data error in a sampling window. Additionally, the channel monitoring logic helps keep track of specific channel information. The measured silicon result shows a total power consumption of 1.2 μW for 1.6 kHz sampling frequency when driven by a 32 kHz clock, which is 8.6x less than prior works. The CDC is also tested with DMMP (dimethyl-methylphosphonate) gas sensor in gas chromatography (GC). Implemented in 65 nm CMOS process, the 10-channel CDC occupies 0.251 mm 2 of active area (0.0251 mm 2 /Ch).

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being.

Elderly health monitoring, rehabilitation training, and sport supervision could benefit from continuous assessment of joint angle, and angular velocity to identify the joint movement patterns. However, most of the measurement systems are designed based on special kinematic sensors to estimate angular velocities. The study aims to measure the lower limb joint angular velocity based on a 2D vision camera system during squat and walking on treadmill action using deep convolution neural network (CNN) architecture. Experiments were conducted on 12 healthy adults, and six digital cameras were used to capture the videos of the participant actions in lateral and frontal view. The normalized cross-correlation (Cc norm ) analysis was performed to obtain a degree of symmetry of the ground truth and estimated angular velocity waveform patterns. Mean Cc norm for angular velocity estimation by deep CNN model has higher than 0.90 in walking on the treadmill and 0.89 in squat action. Furthermore, joint-wise angular velocities at the hip, knee, and ankle joints were observed and compared. The proposed system gets higher estimation performance under the lateral view and the frontal view of the camera. This study potentially eliminates the requirement of wearable sensors and proves the applicability of using video-based system to measure joint angular velocities during squat and walking on a treadmill actions.