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The physical ability of the elderly will decrease along with the aging process, thus increasing the potential for falls. An elderly person who has experienced a fall and is not detected for a long time will bring many possible consequences. Early detection of an elderly fall will help to minimize this possibility by reducing the time between the oc...
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... first step taken at this stage of the analysis is to validate by ensuring that the system is made in accordance with the needs of the user and able to handle the problems that exist in the user, as well as analyzing the needs of both hardware and software to build the system. Figure 1 is system overview of Fall Detection Clothes. This system uses hardware consisting of 10 IMU sensors (MPU-6050) that are connected to each other in a specially designed shirt. ...
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Embedding sensors into clothing is promising as a way for people to wear multiple sensors easily, for applications such as long-term activity monitoring. To our knowledge, this is the first published dataset collected from sensors in loose clothing. 6 Inertial Measurement Units (IMUs) were configured as a ‘sensor string’ and attached to casual trousers such that there were three sensors on each leg near the waist, thigh, and ankle/lower-shank. Participants also wore an Actigraph accelerometer on their dominant wrist. The dataset consists of 15 participant-days worth of data collected from 5 healthy adults (age range: 28–48 years, 3 males and 2 females). Each participant wore the clothes with sensors for between 1 and 4 days for 5–8 hours per day. Each day, data were collected while participants completed a fixed circuit of activities (with a video ground truth) as well as during free day-to-day activities (with a diary). This dataset can be used to analyse human movements, transitional movements, and postural changes based on a range of features.
Self-powered motion sensors are demanded as an energy-efficient technology for future autonomous applications. However, realizing fully self-powered random motion-sensing at a system level is still challenging. Herein, the dual-mode triboelectric nanogenerators (TENGs) are integrated with an electromagnetic generator (EMG) to realize a self-sustained wireless random motion-sensing system. For EMG, a magnetic repulsion mechanism is introduced which equally assists the self-actuation of sliding TENG (S-TENG) and contact separation TENG (CS-TENG) simultaneously. To accomplish self-actuation on CS-TENG, a double-layered flexible thin film of PDMS-FeSiCr/PDMS is used which can simultaneously work as an active TENG layer as well as actuating layer thereby reducing the operational complexity. The motion-sensing resolution and sensitivity of S-TENG are improved by implementing interdigitated electrodes and surface modifications, whereas the self-actuating CS-TENGs offer precise detection of motion directions. The dual-mode TENGs combinedly can measure random motion parameters and motion directions along linear, rotational, and tilting conditions accurately. Moreover, the self-powered motion sensors exhibit excellent acceleration (1.966 V.s²/m), frequency (14.099 V/Hz), and tilting angle (0.257 V/degree) sensitivity with greater accuracy. The electricity generated from EMG (peak power: 103.88 mW) is sufficient to operate signal processing and transmission components for self-powered wireless control of the robotic ball balancing platform, showing future possibilities for self-powered autonomous control applications.
