Yutaka Kuwae

Quantitative analysis of fall risk using TUG test

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 subjects 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.

A fully automated health-care monitoring at home without attachment of any biological sensors and its clinical evaluation

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.

Development of a Wearable Sensor System for Monitoring Static and Dynamic Posture together with Walking Speed for Use in Rehabilitation

We have developed a wearable system for monitoring static and dynamic posture in sagittal plane together with walking speed, and reported its usefulness in the rehabilitation field. However, in the previous system, there still remain several drawbacks for practical use such as accuracy in angle measurement, cumbersome cable arrangements, and so on. In order to improve these practical drawbacks, a new sensor system was designed, and its availability was evaluated. Using 11 healthy subjects (21 to 75 yrs) and 14 patients with hemiplegia (61 to 87 yrs) under their informed consent, the measuring accuracy of the improved system was evaluated by simultaneous recordings of a digital video. The results demostrated that the accuracy of this system (angle change; r=0.997, walking speed; r=0.992) showed superior to that of the previous system (angle change; r=0.986, walking speed; r=0.960). It is clearly shown through the practical monitoring that this system appears to be a useful and significant means for quantitative assessment of the human motion during rehabilitation programs.

Development of an ambulatory device for monitoring posture change and walking speed for use in rehabilitation.

Monitoring of posture change in sagittal plane and walking speed is important for evaluate the effectiveness of rehabilitation program or brace. We have developed a wearable device for monitoring human activity. However, in the previous system, there still remain several drawbacks for practical use such as accuracy in angle measurement, cumbersome cable arrangements, and so on. In order to improve these practical drawbacks, a new sensor system was designed, and its availability was evaluated. The results demonstrated that the accuracy of this system showed superior to that of the previous, and this system appears to be a significant means for quantitative assessment of the patients motion

Quantitative analysis of the fall-risk assessment test with wearable inertia sensors

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.

Wearable Inertia Sensor Application in the Rehabilitation Field

For the aged society, the physical activity of daily living is important to improve the quality of life. The simple quantitative evaluation of physical activity as well as rehabilitation is required We have developed wearable inertia sensors as well as evaluating system. We evaluated parameters such as RMS and autocorrelation function of stride and step for common neuro-physiological test in rehabilitation. The normal and fall-risk subjects were performed the 10 m trial. Furthermore obtained data from the wearable motion sensor were compared to the general estimation parameters such as performing time and Activities of Daily Living (ADL) score. In 10 meter trial, the result indicated that the ADL score is weakly correlated to the RMS of acceleration signal. However, the relationship between walking speed and RMS was highly correlated. The ADL score is general daily living activities and the walking is one of daily activities. The walking speed ,RMS and autocorrelation function of step and stride were significantly different between normal and fall-risk subjects. In conclusion, the obtained acceleration and angular velocity signals may help us the evaluation of daily activities and rehabilitation training quantitatively.

New attempt of proposing the pedometer algorithm in the elderly

A pedometer is popular as a quantitative evaluator of walking. However it may not measure the number of steps for elderly. In this study, we attempted to propose a step count algorithm based on a filter bank and a threshold processing to improve the accuracy of an accelerometer-type step counter for elderly. The waist acceleration signals during a self-paced walking were recorded form 74 participants of the gait training at the commuting rehabilitation services center using triaxial accelerometer. A conventional accelerometer-type pedometer was also attached close to the accelerometer. They walked approximately 20 m. As a result of the algorithm verification using 74 elderly data sets, 57 subjects could be estimated with less than 10% error. We suggest that our step count algorithm is suitable for estimating the number of steps for elderly.

The influence of floor material on standing and walking by hemiplegic patients

In this study, we evaluated the influence of floor materials on standing and walking in hemiplegic patients. To monitor body motion during standing and walking without any constraint, we used a measurement system that consisted of an accelerometer device, a telemeter system, and a personal computer. The posture angles in the antero-posterior and lateral directions were calculated from the low frequency component of the acceleration signal to evaluate body motion. Experiments were performed with six poststroke hemiplegic patients. We modified the time up and go test introduced by Podsiadle. The patients executed the task on three different floor materials: wooden flooring, linoleum, and carpet. The posture angle pattern on carpet differed from those on wooden flooring and linoleum. Therefore, the floor material influenced body motion. We suspect that this difference in movement corresponds to the hardness of the material.

Improved Wearable Monitoring System for Posture Changes and Walking Speed and its Application to Supporting Physical Therapist in Rehabilitation

In order to evaluate the effectiveness of rehabilitation, the physical therapists must assess subjectively such posture changes in patients as standing-up, walking, and so on, by direct observation. There is, therefore, a need of quantitative assessment of such activities. From this viewpoint, we have been developing a device for ambulatory monitoring of posture changes and walking speed. This monitoring system could measure angle changes of a subject’s trunk, thigh and calf on the sagittal plane using a jacket-typed and a knee-supporter-typed sensor unit, and also calculate walking speed from the thigh and calf angle changes. In order to investigate its applicability to patients’ monitorings in a rehabilitation center, we have developed a new monitoring system which can display static and dynamic motion pictures as well as detailed angle changes in a convenient way for the physical therapist. Using six patients with hemiplegia, the patients’ motions were successfully monitored for a longer period of time during walking training in the rehabilitation center. The results clearly demonstrated that the system could detect detailed motion characteristics during walking as angle changes between specific body segments and walking speed, indicating that the system appears useful for evaluating quantitatively the effectiveness of rehabilitation program.