We examined falling risk among elderly using a wearable inertial sensor, which combines accelerometer and gyrosensors devices, applied during the Timed Up and Go (TUG) test. Subjects were categorised into two groups as low fall risk and high fall risk with 13.5 s duration taken to complete the TUG test as the threshold between them. One sensor was attached at the subject's waist dorsally, while acceleration and gyrosensor signals in three directions were extracted during the test. The analysis was carried out in phases: sit-bend, bend-stand, walking, turning, stand-bend and bend-sit. Comparisons between the two groups showed that time parameters along with root mean square (RMS) value, amplitude and other parameters could reveal the activities in each phase. Classification using RMS value of angular velocity parameters for sit-stand phase, RMS value of acceleration for walking phase and amplitude of angular velocity signal for turning phase along with time parameters suggests that this is an improved method in evaluating fall risk, which promises benefits in terms of improvement of elderly quality of life.
We performed a quantitative analysis of the fall-risk assessment test using a wearable inertia sensor focusing on two tests: the time up and go (TUG) test and the four square step test (FSST). These tests consist of various daily activities, such as sitting, standing, walking, stepping, and turning. The TUG test was performed by subjects at low and high fall risk, while FSST was performed by healthy elderly and hemiplegic patients with high fall risk. In general, the total performance time of activities was evaluated. Clinically, it is important to evaluate each activity for further training and management. The wearable sensor consisted of an accelerometer and angular velocity sensor. The angular velocity and angle of pitch direction were used for TUG evaluation, and those in the pitch and yaw directions at the thigh were used for FSST. Using the threshold of the angular velocity signal, we classified the phase corresponding to each activity. We then observed the characteristics of each activity and recommended suitable training and management. The wearable sensor can be used for more detailed evaluation in fall risk management. The wearable sensor can be used more detailed evaluation for fall-risk management test.
To evaluate the effectiveness of rehabilitation, physical therapists must assess the posture changes in patients standing-up, walking, etc. Conventional subjective assessment of using direct observation or interviews at rehabilitation centers and of the actual physical condition is difficult, calling for the development of objective measurement of the posture change and activity both at rehabilitation centers, and in the home. One way to do so is to record these using a video camera, but the measurement range is usually limited and not useful in rehabilitation. A wearable system for monitoring angle changes in the trunk, thigh, and calf on the sagittal plane together with walking speed we developed earlier, required that the user carry three sensors for the trunk, thigh, and calf and a data logger, and wear cumbersome cables. To eliminate this practical drawback, we designed a new sensor for rehabilitation and quantitatively assessed posture change during rehabilitation and activity in daily living using the new system. We combined the previous four units into two – a jacket-typed trunk unit holding a data logger and a sensor for measuring trunk angle change and a knee-supporter-typed lower limb sensors containing two angular sensors – greatly simplifying the cumbersome cable assembly. We measured activity in eight rehabilitation subjects and four subjects during daily living using the wearable device. Results demonstrated that our device could measured detailed motion characteristics as angle changes between body segments during rehabilitation, and the rate of four activities – standing, walking, sitting, and lying – during daily living, making it useful in rehabilitation.
We suggest that our FFT method is suitable for estimating the number of steps during walking in this population.
Abstract-Daily monitoring of health condition is important for an effective scheme for early diagnosis, treatment and prevention of lifestyle-related diseases such as adiposis, diabetes, cardiovascular diseases and other diseases. Commercially available devices for health care monitoring at home are cumbersome in terms of self-attachment of biological sensors and self-operation of the devices. From this viewpoint, we have been developing a non-conscious physiological monitor installed in a bath, a lavatory, and a bed for home health care and evaluated its measurement accuracy by simultaneous recordings of a biological sensors directly attached to the body surface. In order to investigate its applicability to health condition monitoring, we have further developed a new monitoring system which can automatically monitor and store the health condition data. In this study, by evaluation on 3 patients with cardiac infarct or sleep apnea syndrome, patients' health condition such as body and excretion weight in the toilet and apnea and hypopnea during sleeping were successfully monitored, indicating that the system appears useful for monitoring the health condition during daily living.
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