Automatic Fall Detectors Research Articles

Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device

This paper proposes an automatic fall detector in a wearable device that can reduce risks by detecting falls and promptly alerting caregivers. For this purpose, we propose cluster-analysis-based user-adaptive fall detection using a fusion of heart rate sensor and accelerometer. The objectives of the proposed fall detector are to have high accuracy with a low-complexity model regardless of diverse conditions. To meet the objectives, we propose the best 13-dimensional feature subset by using feature selection. In addition, we verify the performance increment of combining a heart rate sensor with an accelerometer and the effectiveness of the cluster-analysis-based anomaly detection. We also show the effectiveness of the user-adaptive method when using both heart rate and acceleration signals that were hardly covered in other papers. Finally, we prove that the performance of the proposed fall detector achieves is better than that of recent user-adaptive and user-independent approaches. This study is the first attempt to demonstrate the merits of the user-adaptive approach using a combination of heart rate and acceleration signals to detect falls. Moreover, this paper also contributes to fall detection area by providing the data we collected.

Low-power operation of a barometric pressure sensor for use in an automatic fall detector.

The use of a barometric pressure sensor in a wearable fall detector has been shown to improve the detection accuracy by determining the altitude change associated with the fall event. However, the barometer is a high-power-consuming sensor. This paper proposes a fall detection approach using a hermetically sealed and waterproof enclosure incorporating a small window covered by a semi-permeable membrane (SPM) to delay the equilibrium of internal and external pressures. This feature can be utilized to limit the time the barometer is powered but still capturing critical pressure information to discriminate fall and non-fall events. The proposed fall detection system is evaluated with an existing data set of simulated fall and activities of daily living in which the barometric pressure data are delayed using a mathematical model of the enclosure and SPM assembly. Also, a benchtop test is performed to estimate the power and battery life. The proposed fall detection system achieves 94.0% sensitivity and 90.0% specificity with an estimated battery life of 995.7 days.

Power-Efficient Interrupt-Driven Algorithms for Fall Detection and Classification of Activities of Daily Living

Falls lead to major health problems for the elderly. Immediate help could lower the risk of complications and death and greatly increase the likelihood of returning to independent living. Automatic fall detectors are useful devices that can alert family members and caregivers at those life-critical moments. Traditional accelerometer-based fall studies focus on accuracies and largely neglect the fact that algorithms will mostly be implemented in microcontroller units (MCUs) with limited speed and random access memory. In addition, it is desirable for a fall detector to have a battery life of several weeks or months. This paper presents a fall detection algorithm and a classification algorithm for activities of daily living using a wrist-worn wearable device. Both algorithms are power-efficient and can be implemented easily in an 8-bit MCU. They adopt an interrupt-driven approach based on a modern digital microelectromechanical systems accelerometer which supports interrupts and data buffering. The approach is completely different from conventional algorithms which must examine and process every piece of data sampled at high frequencies. The interrupt-driven approach allows a host MCU to examine significantly less data and only process upon accelerometer or timer interrupts.

Fear of Falling and the Use of Telecare by Older People

Introduction: Since fear of falling has been linked to self-restriction of activity, a further decline in physical functioning and a greater risk of falling, interventions, such as use of telecare, to address a person's fear are essential to reduce the risk of further falls. The aim of this critical review was to evaluate the association between older people's fear of falling and the use of telecare, and whether telecare could reduce this fear. Method: A search of major databases (1980–2011) identified 424 results and, following screening, 10 articles were included in the review. Findings: Perceptions of older people about using telecare were mixed: some felt a greater sense of security and reassurance but, for others, confidence levels were not increased. Studies suggest that an automatic fall detector was useful for older people who were unable or reluctant to use a pendant alarm following a fall. Conclusion: The use of telecare including the wearing of an automatic fall detector, while improving confidence and reducing fear of falling in some, does not suit everyone. A careful assessment of a person's views on his or her falls risk and level of anxiety will determine how telecare can best meet the person's needs to remain supported at home.

Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects

Falling is a common accident among older people. Automatic fall detectors are one method of improving security. However, in most cases, fall detectors are designed and tested with data from experimental falls in younger people. This study is one of the first to provide fall-related acceleration data obtained from real-life falls. Wireless sensors were used to collect acceleration data during a six-month test period in older people. Data from five events representing forward falls, a sideways fall, a backwards fall, and a fall out of bed were collected and compared with experimental falls performed by middle-aged test subjects. The signals from real-life falls had similar features to those from intentional falls. Real-life forward, sideways and backward falls all showed a pre impact phase and an impact phase that were in keeping with the model that was based on experimental falls. In addition, the fall out of bed had a similar acceleration profile as the experimental falls of the same type. However, there were differences in the parameters that were used for the detection of the fall phases. The beginning of the fall was detected in all of the real-life falls starting from a standing posture, whereas the high pre impact velocity was not. In some real-life falls, multiple impacts suggested protective actions. In conclusion, this study demonstrated similarities between real-life falls of older people and experimental falls of middle-aged subjects. However, some fall characteristics detected from experimental falls were not detectable in acceleration signals from corresponding heterogeneous real-life falls.

Fall detectors: Do they work or reduce the fear of falling?

The fear of falling in older people can affect health, well‐being, care needs and quality of life. Fall‐monitoring equipment that can automatically raise a call for assistance is increasingly being developed, and may reduce the fear of falling. This study sought to discover the views of users and providers on technology of this type of and to quantify the impact fall detectors have on the fear of falling. The results of the study suggest that fall‐monitoring equipment is not considered routinely as part of a care package, and knowledge of devices is poor. A trial of automatic fall detectors provided indicated that they may reduce fear of falling for certain users and so should be considered in falls management, but more evidence and understanding are required to appreciate both the benefits and shortfalls of providing equipment of this sort.