The energy cost of level walking (C,) was measured from the ratio of O2 consumption to speed (from 0.1 to 1.2 m-s-') in hemiplegic patients (n=20) and in a control group of healthy subjects (n=17). Average age and body mass were 58, 54 years and 73, 78 kg, respectively. In hemiplegic patients C, was higher than in control subjects (average value at 1 .O m-s-' =3.6 and 3.3 J.m-'.kg-', respectively) and this difference increased at lower speeds (from 5.1% at 1.2 m-s-' to 28.7% at 0.1 m.s-').
Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of <10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke. NIH Public Access
This study compared the performance of surface electromyographic (sEMG) sensors for different detection conditions affecting the electro-mechanical stability between the sensor and its contact with the skin. These comparisons were made to gain a better understanding of how specific characteristics of sensor design and use may alter the ability of sEMG sensors to detect signals with high fidelity under conditions of vigorous activity. The first part of the study investigated the effect of different detection surface contours and adhesive tapes on the ability of the sensor to remain in electrical contact with the skin. The second part of the study investigated the effects of different skin preparations and hydrophilic gels on the production of movement artifact resulting from sinusoidal and impact mechanical perturbations. Both parts of the study evaluated sensor performance under dry skin and wet skin (from perspiration) conditions. We found that contouring the detection surface and adding a more adhesive double-sided tape were effective in increasing the forces needed to disrupt the electrical contact between the electrodes and the skin for both dry skin and wet skin conditions. The mechanical perturbation tests demonstrated that hydrophilic gel applied to the detection surface of the sensor produced greater movement artifacts compared to sensors without gel, particularly when the sensors were tested under conditions in which perspiration was present on the skin. The use of a surfactant skin preparation did not influence the amount of movement artifacts that resulted from either the sinusoidal or impact perturbations. The importance of these findings is discussed in terms of their implications for improving sEMG signal fidelity through sensor design modifications and procedures for interfacing them with the skin.
Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of 10% for the nonidentification tasks.The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke.
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