Due to the demographic change, the need of rehabilitation is rising. Home-based rehabilitation can lower the fnancial burden, support reintegration into daily (work-)life and increase motivation as well as compliance of patients. Several device-supported approaches for rehabilitation were investigated in the research project REHABitation. An insole-based live-feedback system to support patients performing partial weight bearing was developed and tested in a clinical pilot study. Rehabilitative games using commercial gaming control systems like the Microsoft Kinect and the Nintendo Wii Balance Board were developed for range-of-motion and balance training, respectively. For these serious games, usability was tested with the System Usability Scale questionnaire. The comparability of range-of-motion measurements of shoulder movements conducted with inertial measurement units and an optical motion capture system was elaborated. Results fo the clinical study suggest that the patients' compliance with partial weight bearing load restriction was improved with the use of the live-feedback system developed. The use of Wii and Kinect solutions is possible and helps to increase compliance of patients due to high system usability scale scores and positive feedback. The use of intertial measurement units for the detection of motion and its characteristics is highly depending on the used type of system and the intended time span of use. All these approaches were interconnected with diagnosis, corresponding exercises/assessments and tools in the web-based Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
Many cyclists use online-maps for planning their routes, however, only little information is known about the road surface of different cycling paths, farm or public roads. Cyclists prefer road surfaces fitting the type of bike they are using for a specific ride (e.g., time trial, road, MTB, cyclocross, gravel bike). Often riders upload their ride data including GPS, heart rate (HR) or power (P) on platforms like Strava or Garmin Connect. In this research we tried to evaluate whether it is possible to (1) evaluate the road surface quality using a 3D accelerometer mounted on the bicycle's fork (f = 500 Hz) and whether (2) results of similar quality can be achieved using the accelerometer of a smartphone (f = 100 Hz) placed in the cyclist's pocket. For data acquisition a cyclist rode on a cyclocross bicycle on three different road surfaces (cobblestones, gravel and tarmac) with three different speeds (10, 20 and 30 km/h) and three different tire pressures (3, 4 and 5 bar). Data of both measuring systems were analyzed using machine learning algorithms. Results showed that road surfaces could be predicted with more than 99% accuracy with the accelerometer and with more than 97% with the smartphone-data.
“Ride” has been established to subjectively describe the heel-to-toe transition during walking and running. Recently, a study was published aiming to quantify “ride” by linking it to the maximum velocity of the anterior-posterior (AP) progression of the center of pressure (COP) during the first 30% of the stance phase. While that study investigated the parameter when running at a constant velocity of approximately 3.5 m/s (i.e., 12.6 km/h), this study was carried out to evaluate the influence of running velocity on “ride” when running. Five healthy participants performed runs on a treadmill at 8, 10 and 12 km/h with three different running shoes, and their plantar pressure was measured at 300 Hz using pressure-sensing insoles. “Ride” was calculated as suggested by the previously mentioned study. In two of the three shoes, “ride” decreased with increasing running speed. Between the shoes, however, there is no clear image of how the shoes influence this parameter.
While it is assumed that pressure-sensing insoles are usually placed directly below the foot and on top of the shoes’ standard insoles, nearly no previously published study actually describes the procedure, which leaves a slight uncertainty. Therefore, the aim of this study was to evaluate whether the placement has an influence on selected parameters or not. Five healthy participants took part in the measurements and ran on a treadmill at a running velocity of 10 km/h with three different running shoes. Plantar pressure was measured using pressure-sensing insoles, which were once placed on top and once below the shoes’ standard insoles. Selected parameters were the maximum and mean pressure and the range of the center of pressure (COP) in anterior–posterior and medial–lateral directions. The results indicate that maximum and mean pressure decrease when the pressure-sensing insole lies below the shoe’s insole and the medial–lateral COP is the least effected parameter.
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