Fall Detection Devices and Their Use With Older Adults

Telemedicine and e-Health, 2016

Background: Falls are a significant concern for the older adult (OA) population, many of whom are unable to get up following a fall. Introduction: While many devices exist designed to detect a fall, little work has been conducted to evaluate the usability of such devices. We present a longitudinal usability study of a fall detection (FD) device tested with OAs in real-world settings. Materials and Methods: OAs were recruited and asked to use a wearable FD device for up to 4 months. Participants were interviewed at baseline and 2 and 4 months and encouraged to provide direct feedback on their experience. Results: In total, 18 OAs participated in the study. Eight completed the 4-month trial. We conducted a total of 38 interviews (16 baseline, 7 midpoint, and 15 final) and logged a total of 78 comments. While participants enjoyed the GPS and automatic detection features of the device, they were unhappy with the volume of false alarms and obtrusiveness of the device. Many also did not see a great need for having the device or were embarrassed by the device. Discussion: Engineers must work to better develop this technology so that it is accessible to people with hearing loss, limited dexterity, and low vision. Utilizing age-appropriate design techniques will help make such informatics tools more user friendly. Conclusion: We explored the usability of a particular FD device with OAs and provide design recommendations to help future device manufacturers create more age-appropriate devices.

BMC geriatrics, 2016

The involvement of users is recommended in the development of health related technologies, in order to address their needs and preferences and to improve the daily usage of these technologies. The objective of this literature review was to identify the nature and extent of research involving older people in the development of fall detection systems. A scoping review according to the framework of Arksey and O'Malley was carried out. A key term search was employed in eight relevant databases. Included articles were summarized using a predetermined charting form and subsequently thematically analysed. A total of 53 articles was included. In 49 of the 53 articles, older people were involved in the design and/or testing stages, and in 4 of 53 articles, they were involved in the conceptual or market deployment stages. In 38 of the 53 articles, the main focus of the involvement of older people was technical aspects. In 15 of the 53 articles, the perspectives of the elderly related to t...

Patient Preference and Adherence

Purpose: To explore the needs and preferences of community-dwelling older people, by involving them in the device design and mock-up development stage of a fall detection device, consisting of a body-worn sensor linked to a smartphone application. Patients and methods: A total of 22 community-dwelling persons 75 years of age and older were involved in the development of a fall detection device. Three semistructured focus group interviews were conducted. The interview data were analyzed using qualitative descriptive analysis with deductive coding. Results: The mock-up of a waterproof, body-worn, automatic and manual alerting device, which served both as a daytime wearable sensor and a night-time wearable sensor, was welcomed. Changes should be considered regarding shape, color and size along with alternate ways of integrating the sensor with items already in use in daily life, such as jewelry and personal watches. The reliability of the sensor is key for the participants. Issues important to the alerting process were discussed, for instance, who should be contacted and why. Several participants were concerned with the mandatory use of the smartphone and assumed that it would be difficult to use. They criticized the limited distance between the sensor and the smartphone for reliable fall detection, as it might restrict activity and negatively influence their degree of independence in daily life. Conclusion: This study supports that involving end users in the design and mock-up development stage is welcomed by older people and allows their needs and preferences concerning the fall detection device to be explored. Based on these findings, the development of a "need-driven" prototype is possible. As participants are doubtful regarding smartphone usage, careful training and support of community-dwelling older people during real field testing will be crucial.

2016

This thesis consists of material all of which I authored or co-authored: see Statement of Contributions included in the thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public.

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016

In this paper, we describe two longitudinal studies in which fall detection sensor technology was tested in the homes of older adults. The first study tested Doppler radar, a twowebcam system, and a depth camera system in ten apartments for two years. This continuous data collection allowed us to investigate the real-world setting of target users and compare the advantages and limitations of each sensor modality. Based on this study, the depth camera was chosen for a current ongoing study in which depth camera systems have been installed in 94 additional older adult apartments. We include a discussion of the different sensor systems, the pros and cons of each, and results of the fall detection and false alarms in the older adult homes.

Journal of the American Geriatrics Society, 2015

To the Editor A significant danger with falling is the inability to get up afterwards, which is reported to occur in as much as 30% of falls (1). Individuals have been shown to have better chances of survival the faster they are discovered following a fall (5,7). Thus, there have been several devices designed to detect a fallen individual. We conducted a pilot study to investigate the real world use and accuracy of a wearable fall detection (FD) device with community dwelling older adults (OAs). The device had the ability to automatically detect falls using a combination of accelerometer, magnetometer and gyroscope. It also had audio feedback and GPS capabilities. The company reported training their device in a laboratory setting with subjects performing prescribed falls, ADLs, and stumbles. The system was subsequently tested on an independent data set and yielded results of sensitivity range from 94.1-94.4% and specificity from 92.1-94.6%. Eighteen participants participated in the four month study (8 completers). Of the 10 Partial Completers, 9 voluntarily left the study, while one was unable to complete due to an injurious fall. Participants had the device for an average of 80.7 days (range 8-124). A total of 84 alarms indicating a fall were recorded, of which 83 were false alarms. The largest percentage of false alarms (42.2%) were during normal device use. Another 16.9% of false alarms occurred when the participant dropped the device. Device misuse and putting down the device each constituted 10.8% of false alarms. Finally 19.3% of false alarms occurred for unknown reasons. Table 1 shows the binary classification measurements for the study.

Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare, 2012

Falls are the leading cause of disability and injuryrelated deaths among older adults, resulting in over 1.6 million annual emergency hospitalizations in the United States. Fall detection devices often rely on dramatized falls when developing algorithms. This study used tri-axial accelerometers worn by older adult research subjects in order to (1) collect false positive data (2) capture potential fall events and (3) evaluate the usability of the device among this target population. Twelve older adults wore activity monitors while participating in structured and unstructured activities. The study collected data on 120 patient days, yielding 492.5 hours of monitored time. Actigraphy data of annotated activities were used to define parameters for refining the algorithm. No falls occurred during the study, but valuable false positive data were collected. The study also obtained information on the usability of the devices and revealed user perspectives on commercializing the final product.

JMIR Aging, 2021

There is an exponential increase in the range of digital products and devices promoting aging in place, in particular, devices aiming at preventing or detecting falls. However, their deployment is still limited and only few studies have been carried out in population-based settings owing to the technological challenges that remain to be overcome and the barriers that are specific to the users themselves, such as the generational digital divide and acceptability factors specific to the older adult population. To date, scarce studies consider these factors. To capitalize technological progress, the future step should be to better consider these factors and to deploy, in a broader and more ecological way, these technologies designed for older adults receiving home care to assess their effectiveness in real life.

advanced visual interfaces, 2020

Sensors

Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification model...