Now showing 1 - 3 of 3
  • Publication
    Feature-Based Evaluation of a Wearable Surface EMG Sensor against Laboratory Standard EMG during Force-Varying and Fatiguing Contractions
    Recent advances in wearable sensors enable recording of electromyography (EMG) outside the laboratory for extended periods of time. However, the properties of wearable EMG systems designed for long-term recording may differ from those of laboratory-standard systems, potentially impacting data. This study evaluated EMG features derived from signals recorded using a wearable system (BioStampRC, MC10 Inc.) against a reference laboratory system (Bagnoli, Delsys Inc.). Surface EMG data from the biceps brachii were recorded simultaneously using both systems during isometric elbow flexion, between 10% and 80% of maximum voluntary contraction (MVC), and during sustained submaximal fatiguing contraction, in twelve subjects. Linear and nonlinear EMG temporal and spectral features were then compared across both systems. No effect of recording system was detected on EMG onset/offset times, or on the relationship between force and EMG root mean squared amplitude. However, the relationships between force and median frequency, percentage determinism and multiscale entropy differed between systems. Baseline noise was also greater for the BioStampRC. Lower median frequencies were observed for the wearable system, likely due to the larger interelectrode distance, however, the relative change in EMG amplitude and median frequency during the fatiguing contraction was similar for both. Percentage determinism increased and multiscale entropy decreased during the fatiguing contraction for both systems, with higher and lower values respectively for the wearable system. Results indicate that the BiostampRC is appropriate for EMG onset/offset and amplitude estimation. However, caution is advised when comparing across systems as spectral and nonlinear features may differ due to electrode design differences.
    Scopus© Citations 28  13
  • Publication
    Regression-based analysis of front crawl swimming using upper-arm mounted accelerometers
    Wearable accelerometers can be used to quantify movement during swimming, enabling objective performance analysis. This study examined arm acceleration during front crawl swimming, and investigated how accelerometer-derived features change with lap times. Thirteen participants swam eight 50m laps using front crawl with a tri-axial accelerometer attached to each upper arm. Data were segmented into individual laps; lap times estimated and individual strokes extracted. Stroke times, root mean squared (RMS) acceleration, RMS jerk and spectral edge frequencies (SEF) were calculated for each stroke. Movement symmetry was assessed as the ratio of the minimum to maximum feature value for left and right arms. A regularized multivariate regression model was developed to estimate lap time using a subset of the accelerometer-derived features. Mean lap time was 56.99±11.99s. Fifteen of the 42 derived features were significantly correlated with lap time. The regression model included 5 features (stroke count, mean SEF of the X and Z axes, stroke count symmetry, and the coefficient of variation of stroke time symmetry) and estimated 50m lap time with a correlation coefficient of 0.86, and a cross-validated RMS error of 6.38s. The accelerometer-derived features and developed regression model may provide a useful tool to quantitatively evaluate swimming performance.
  • Publication
    Estimation of respiration rate and sleeping position using a wearable accelerometer
    Wearable inertial sensors offer the possibility to monitor sleeping position and respiration rate during sleep, enabling a comfortable and low-cost method to remotely monitor patients. Novel methods to estimate respiration rate and position during sleep using accelerometer data are presented, with algorithm performance examined for two sensor locations, and accelerometer-derived respiration rate compared across sleeping positions. Eleven participants (9 male; aged: 47.82±14.14 years; BMI 30.9±5.27 kg/m 2 ; AHI 5.77±4.18) undergoing a scheduled clinical polysomnography (PSG) wore a tri-axial accelerometer on their chest and upper abdomen. PSG cannula flow and position data were used as benchmark data for respiration rate (breaths per minute, bpm) and position. Sleeping position was classified using logistic regression, with features derived from filtered acceleration and orientation. Accelerometer-derived respiration rate was estimated for 30 s epochs using an adaptive peak detection algorithm which combined filtered acceleration and orientation data to identify individual breaths. Sensor-derived and PSG respiration rates were then compared. Mean absolute error (MAE) in respiration rate did not vary between sensor locations (abdomen: 1.67±0.37 bpm; chest: 1.89±0.53 bpm; p=0.52), while reduced MAE was observed when participants lay on their side (1.58±0.54 bpm) compared to supine (2.43±0.95 bpm), p<; 0.01. MAE was less than 2 bpm for 83.6% of all 30 s windows across all subjects. The position classifier distinguished supine and left/right with a ROC AUC of 0.87, and between left and right with a ROC AUC of 0.94. The proposed methods may enable a low-cost solution for in-home, long term sleeping posture and respiration monitoring.
      141Scopus© Citations 20