Effect of a Passive Exosuit on Sit-to-Stand Performance in Geriatric Patients Measured by Body-Worn Sensors-A Pilot Study.

Standing up from a seated position is a prerequisite for any kind of physical mobility but many older persons have problems with the sit-to-stand (STS) transfer. There are several exosuits available for industrial work, which might be adapted to the needs of older persons to support STS transfers. However, objective measures to quantify and evaluate such systems are needed. The aim of this study was to quantify the possible support of an exosuit during the STS transfer of geriatric patients. Twenty-one geriatric patients with a median age of 82 years (1.-3.Q. 79-84 years) stood up at a normal pace (1) from a chair without using armrests, (2) with using armrests and (3) from a bed with pushing off, each condition with and without wearing an exosuit. Peak angular velocity of the thighs was measured by body-worn sensors. It was higher when standing up with exosuit support from a bed (92.6 (1.-3.Q. 84.3-116.2)°/s versus 79.7 (1.-3.Q. 74.6-98.2)°/s; p = 0.014) and from a chair with armrests (92.9 (1.-3.Q. 78.3-113.0)°/s versus 77.8 (1.-3.Q. 59.3-100.7)°/s; p = 0.089) compared to no support. There was no effect of the exosuit when standing up from a chair without using armrests. In general, it was possible to quantify the support of the exosuit using sensor-measured peak angular velocity. These results suggest that depending on the STS condition, an exosuit can support older persons during the STS transfer.

Assessment of Thigh Angular Velocity by an Activity Monitor to Describe Sit-to-Stand Performance.

The assessment of sit-to-stand (STS) performance is highly relevant, especially in older persons, but testing STS performance in the laboratory does not necessarily reflect STS performance in daily life. Therefore, the aim was to validate a wearable sensor-based measure to be used under unsupervised daily life conditions. Since thigh orientation from horizontal to vertical is characteristic for STS movement, peak angular velocity (PAV) of the thigh was chosen as the outcome variable. A total of 20 younger and older healthy persons and geriatric patients (mean age: 55.5 ± 20.8 years; 55% women) with a wide range of STS performance were instructed to stand up from a chair at their usual pace. STS performance was measured by an activity monitor, force plates, and an opto-electronic system. The association between PAV measured by the thigh-worn activity monitor and PAV measured by the opto-electronic system (gold standard) was r = 0.74. The association between PAV measured by the thigh-worn activity monitor and peak power measured by force plate and opto-electronic system was r = 0.76. The Intra-Class Coefficient (ICC) of agreement between the 2 trials was ICC(A,1) = 0.76. In this sample of persons with a wide range of physical performance, PAV as measured by a thigh-worn acceleration sensor was a valid and reliable measure of STS performance.

Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping.

Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cases of misclassification, stair climbing was falsely detected as walking, with only 0.2-5% not assigned to any of the chosen types of locomotion. Our results demonstrate a promising approach to recognize and differentiate human locomotion within a variety of daily activities.