Rapid progress of ICT technologies have made it possible to monitor our health conditions in daily life for 24 hours. Physical activity in daily life is the easiest parameter to be monitored and it is the most important information indicating our health condition for the extension of healthy life expectancy. The author has studied the monitoring of physical activity in daily life and its clinical applications for more than 30 years. In this paper, monitoring of physical activity in daily life using an acceleration sensor is reviewed.

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Some studies on monitoring of vital signs by sequential image acquisition and processing are introduced. Pulse waveform monitoring at the wrist, and respiration and body movement monitoring during sleep are explained. Current status of development of image processing for real-time detection of pulse waveform, respiration and oxygen saturation of peripheral artery (SpO₂) are also explained. Applications of sequential image processing for evaluating urination and behavioral and psychological symptoms of dementia are explicated.

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By bringing the ability to track physiological signals over long periods of time, wearable sensing has the potential to enable new paradigms in disease prevention, treatment and management, in and beyond healthcare. Such paradigms could range from chronic monitoring to observe and detect gradual shifts in relevant parameters to the discovery of rare and asymptomatic biomarkers. The two use cases described in this article are positioned somewhere within this continuum and demonstrate the unique opportunities of this technology, while at the same time demonstrating its challenging requirements and limitations.

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The decrease in muscular strength of the lower limbs due to aging is a serious problem for elderly people, since it relates to locomotive disability and falls and consequently decreases the quality of life. However, according to the suggestions in previous studies, we can prevent the muscular strength from decreasing by performing appropriate strength training in daily life. To advance the training appropriately, it is necessary to evaluate current muscular strength objectively and to ascertain effectiveness of the training. However, the access to resources for measuring muscular strength is currently limited for many people. Therefore, we have developed a home-use method to evaluate muscular strength. With regard to usability under general home conditions, it is important that users can intuitively understand the result of the muscular strength test. Therefore, we define a new index of muscular strength of the lower limbs, which we named “muscular strength margin”. This index shows the muscular strength normalized by the minimally required muscular strength for daily activities. This index is easier to understand than the muscular strength itself. For example, if the index does not reach 100%, the result means that the muscular strength is lower than the level required for daily life. When the index is 200%, the result means that the muscle is twice as strong as the required level. Additionally, for easy measurement and cost reduction, we utilize a micro-electro-mechanical system acceleration sensor and a gyro sensor, instead of the force sensor, to measure muscle strength. To evaluate the practical validity of “muscular strength margin” as a new index of muscular strength, the muscular strength margin was measured in 98 subjects. The results were consistent with those of previous studies. Additionally, the findings suggest that the value of 100% is a valid guide to evaluate the presence or absence of sufficient muscular strength of the lower limbs. The current method introduced in this article has potential as a beneficial home-use method of evaluating muscular strength.

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The advent of the smartphone, a compact but powerful and multifunctional computing device, permits the measurements of various physiological indices using a commercially available device alone. This is achieved by using built-in flash LED as a light source and built-in CMOS camera as a photo detector, and is called smartphone photoplethysmography (PPG). Although smartphone PPG is so easy that even a non-specialist can operate by oneself in daily life, it should be used with sufficient knowledge of its effectiveness and limitation. Otherwise, users may encounter unpredictable pitfalls. In this review article, we describe the basics of smartphone PPG in terms of the general background of PPG, restrictions especially for smartphone PPG, and caution during measurement, which are essential information for effective daily life measurement.

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Rapid aging of Japan's population has caused serious traffic problems in the country. In 2015, over half of the fatalities in traffic accidents were elderlies. However, only a few studies have investigated the elderly drivers', cyclists' and pedestrians' behaviors in real traffic conditions. We have developed a system to evaluate elderly persons' driving/riding/walking skills from the viewpoint of preventing accidents, using small wireless wearable sensors that directly measure their behaviors. In this paper, we study characteristics of risky behaviors in the elderly. Through an experiment involving 749 elderly drivers, we found that elderly drivers' tendency to neglect proper scanning to confirm safety contributed to their hazardous driving performance. We also found that elderly persons with no driving license behaved in a more hazardous manner while walking or riding bicycle compared to those with license.

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Recently, wearable devices have attracted attention as a technology that can support sports training and healthcare in daily life. Some wearable devices for these applications require measurement of heart rate and/or electrocardiogram (ECG). However, disposable electrodes generally used for ECG measurement are not suited to the measurement in daily life because of skin irritation and other problems. Textile electrodes have high potential to solve these problems. In this paper, we summarize some textile electrodes for ECG measurement, including shirt-type electrodes that we developed.

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Sleep is essential for the maintenance of human life. Polysomnography (PSG) is a common method for the evaluation of sleep quality. However, the test requires attachment of many electrodes to the subject, and the constraint may cause great stress. Therefore, it is important to find a new method to free the subject from being burdened by electrodes;that is, to measure in an unconstraint and noncontact manner. In this study, we propose a new sleep measurement system using microwave sensing. The sensor detects body movements and respiratory movements in a noncontact manner. In this study, we analyzed sleep stage using PSG and simultaneously measured body movements and respiratory movements using a radio-frequency sensor. The mean and standard deviation of the number of body movements decreased, and the movements also became micro-movements when the degree of awakening decreased. The mean respiratory frequency did not change markedly in various sleep stages and showed little dispersion. The mean and standard deviation of the respiratory amplitude decreased as was observed for body movement, and the respiratory amplitude was stable when the degree of awakening decreased. We used the linear discriminant function to classify the sleep stages into:Wake, REM, Light and Deep sleep. The mean agreement rate was approximately 78% for four stages in total. Our results show that the sleep stages can be successfully estimated by body and respiratory movements during sleep.

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An electromyographic (EMG) measuring system has been developed for controlling active upper limb orthoses for patients with Erb's palsy. Patients with Erb's palsy cannot move either their shoulders or elbows, but can move their wrists. Therefore, if they can move their shoulders and elbows by an orthosis, their activities of daily living (ADL) can be improved dramatically. To control the orthosis, EMG measurements of the four wrist movements of extension, flexion, ulnar deviation, and radial deviation are used. However, there is a risk of an erroneous motion, when a movement other than that intended is performed. Therefore, the discriminant error rate of the EMG measurement system should be reduced. Various channel configurations (eight, six, and four channels) were compared for measuring the EMG, and various machine learning techniques;namely, Euclidean distance (euc), Mahalanobis distance (mh), and a support vector machine (svm), were used to discriminate the movement. By defining a threshold voltage and degree of similarity, and by using the most preferred discrimination process, discriminant error rates decreased. Moreover, when the number of motions in training data for discrimination was increased from four to six by adding finger extension and flexion, the discriminant error rates using euc and svm also decreased. However, when the number of channels was reduced from eight to six or four, discriminant error rates increased. Overall, the svm method achieved the highest discriminant rate and lowest discriminant error rate. Finally, an online discrimination method was developed which achieved no difference in discriminant rate and output the results every 710ms.

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