A clinical possibility: preventing health problems after the age of 65

Triboelectric Nanogenerators for Preventive Health Monitoring

With the improvement in life quality, the increased focus on health has expedited the rapid development of portable preventative-health-monitoring devices. As one of the most attractive sensing technologies, triboelectric nanogenerators (TENGs) are playing a more and more important role in wearable electronics, machinery condition monitoring, and Internet of Things (IoT) sensors. TENGs possess many advantages, such as ease of fabrication, cost-effectiveness, flexibility, material-selection variety, and the ability to collect low-frequency motion, offering a novel way to achieve health monitoring for human beings in various aspects. In this short review, we initially present the working modes of TENGs based on their applications in health monitoring. Subsequently, the applications of TENG-based preventive health monitoring are demonstrated for different abnormal conditions of human beings, including fall-down detection, respiration monitoring, fatigue monitoring, and arterial pulse monitoring for cardiovascular disease. Finally, the discussion summarizes the current limitations and future perspectives. This short review encapsulates the latest and most influential works on preventive health monitoring utilizing the triboelectric effect for human beings and provides hints and evidence for future research trends.

Real-time Action Recognition and Fall Detection Based on Smartphone

This paper presents a smartphone application which has realized action recognition and fall detection. The application identifies the holding pattern of smartphone by the data of light sensor, distance sensor and accelerometer sensor, which reduce the impact of recognition resulting from the smartphone's different positions. And then the application uses data collected from the acceleration sensor, the direction angle sensor and the gyro sensor to distinguish fall from daily actions. The results of human motion recognition are uploaded to the server. For the purpose of real time, the network stability of the application is improved by the method of multi-layer detection based on heartbeat packet. Experiments prove that the way of improving network stability can reduce the rate of losing packet. The accuracy of action recognition achieves more than 90%.

Automatic segmentation of triaxial accelerometry signals for falls risk estimation

Falls-related injuries in the elderly population represent one of the most significant contributors to rising health care expense in developed countries. In recent years, falls detection technologies have become more common. However, very few have adopted a preferable falls prevention strategy through unsupervised monitoring in the free-living environment. The basis of the monitoring described herein was a self-administered directed-routine (DR) comprising three separate tests measured by way of a waist-mounted triaxial accelerometer. Using features extracted from the manually segmented signals, a reasonable estimate of falls risk can be achieved. We describe here a series of algorithms for automatically segmenting these recordings, enabling the use of the DR assessment in the unsupervised and home environments. The accelerometry signals, from 68 subjects performing the DR, were manually annotated by an observer. Using the proposed signal segmentation routines, an good agreement was observed between the manually annotated markers and the automatically estimated values. However, a decrease in the correlation with falls risk to 0.73 was observed using the automatic segmentation, compared to 0.81 when using markers manually placed by an observer.

An adapted Gaussian mixture model approach to accelerometry-based movement classification using time-domain features

The accurate classification of everyday movements from accelerometry data will provide a significant step towards the development of effective ambulatory monitoring systems for falls detection and prediction. The search continues for optimal front-end processing methods for use in accelerometry systems. Here, we propose a novel set of time domain features, which achieve a mean accuracy of 91.3% in distinguishing between three postures (sitting, standing and lying) and five movements (sit-to-stand, stand-to-sit, lie-to-stand, stand-to-lie and walking). This is a 39.2% relative improvement in error rate over more commonly used frequency based features. A method for adapting Gaussian Mixture Models to compensate for the problem of limited user-specific training data is also proposed and investigated. The method, which uses Bayesian adaptation, was found to improve classification performance for time domain features, offering a mean relative improvement of 20.2% over a non subject-specific system and 4.5% over a system trained using subject specific data only.

Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement

Accelerometry offers a practical and low cost method of objectively monitoring human movements, and has particular applicability to the monitoring of free-living subjects. Accelerometers have been used to monitor a range of different movements, including gait, sit-to-stand transfers, postural sway and falls. They have also been used to measure physical activity levels and to identify and classify movements performed by subjects. This paper reviews the use of accelerometer-based systems in each of these areas. The scope and applicability of such systems in unsupervised monitoring of human movement are considered. The different systems and monitoring techniques can be integrated to provide a more comprehensive system that is suitable for measuring a range of different parameters in an unsupervised monitoring context with free-living subjects. An integrated approach is described in which a single, waist-mounted accelerometry system is used to monitor a range of different parameters of human movement in an unsupervised setting.