Fall detection using smart floor sensor and supervised learning

Minvielle, Ludovic; Atiq, Mounir; Serra, Renan; Mougeot, Mathilde; Vayatis, Nicolas

doi:10.1109/embc.2017.8037597

Cited by 21 publications

(18 citation statements)

References 8 publications

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“…Cameras [ 12 , 57 ] and floor sensors [ 14 , 58 ] are frequently used for fall detection. Camera captured images were analyzed using a computer vision-based technique to detect falls in real-time [ 13 , 59 ]. Floor sensors detect falls by measuring data that vary in response to movement on the floor [ 60 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Elderly Perception on the Internet of Things-Based Integrated Smart-Home System

Hoon

Hyun

2021

Sensors

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An integrated smart home system (ISHS) is an effective way to improve the quality of life of the elderly. The elderly’s willingness is essential to adopt an ISHS; to the best of our knowledge, no study has investigated the elderly’s perception of ISHS. Consequently, this study aims to investigate the elderly’s perception of the ISHS by comprehensively evaluating its possible benefits and negative responses. A set of sensors required for an ISHS was determined, and interviews were designed based on four factors: perceived comfort, perceived usability, perceived privacy, and perceived benefit. Subsequently, technological trials of the sensor-set followed by two focus group interviews were conducted on nine independently living elderly participants at a senior welfare center in South Korea. Consistent with previous studies, the results of this investigation indicate that elderly participants elicited negative responses regarding usability complexity, and discomfort to daily activities. Despite such negative responses, after acquiring enough awareness about the ISHS’s benefits, the elderly acknowledged its necessity and showed a high level of willingness. Furthermore, these results indicate that for a better adoption of an ISHS, sufficient awareness regarding its benefits and development of elderly-friendly smart home sensors that minimize negative responses are required.

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Section: Literature Reviewmentioning

confidence: 99%

Elderly Perception on the Internet of Things-Based Integrated Smart-Home System

Hoon

Hyun

2021

Sensors

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“…One study [ 56 ] used an electrode on a tripod to measure the effect of capacitive coupling. Another study [ 66 ] used a piezoelectric polymer known to emit electric fields when stress is applied. This polymer was applied to the floor and used to detect different floor-impact activities (such as walking with one or more people).…”

Section: Resultsmentioning

confidence: 99%

“…These sensors are more widely available and range in price but are often cheaper than RF-based technologies. However, these sensors are often limited in range and require multiple sensors to register events throughout a whole house setup (eg, sensors on doors or walls [ 46 , 72 ] or modified beds or blankets for all older adults in a care home [ 61 , 73 ]), and some sensors also require permanent and/or prominent structural changes to the environment (eg, implementing smart tiles [ 66 ] or adding sensors to the ceiling [ 46 , 74 ]), which adds additional costs to the actual implementation compared with RF-based technologies, which are often isolated boxes that offer a larger (whole house) coverage with a minimum amount of sensors [ 68 ]. For wide-scale adoption in older adult care, it is recommended to look for a solution by weighing the costs of development and deployment.…”

Section: Resultsmentioning

confidence: 99%

“…These studies enlisted the various advantages of USSs for the older adults and their caregivers. From the perspective of the older adults, the included studies highlight that USSs are comfortable [ 18 ], do not require technical competency [ 25 ], are privacy aware, require less (to no) attention and compliance [ 72 , 75 ], are affordable [ 50 ], and can operate in NLOS situations [ 66 , 68 ]. From the perspective of caregivers, USSs are ubiquitous in nature [ 57 ], enable continuous monitoring [ 46 ], are easy to integrate [ 50 ], are prone to noisy environments [ 48 ], provide security [ 18 ], and are safe to use with older adults [ 47 ].…”

Section: Resultsmentioning

confidence: 99%

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Implementation of Unobtrusive Sensing Systems for Older Adult Care: Scoping Review

Sharma¹,

Brinke²,

Gemert-Pijnen³

et al. 2021

JMIR Aging

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Background The continuous growth of the older adult population will have implications for the organization of health and social care. Potentially, in-home monitoring unobtrusive sensing systems (USSs) can be used to support formal or informal caregivers of older adults, as they can monitor deviant physical and physiological behavior changes. Most existing USSs are not specific to older adult care. Hence, to facilitate the implementation of existing USSs in older adult care, it is important to know which USSs would be more suitable for older adults. Objective This scoping review aims to examine the literature to identify current USSs for monitoring human activities and behaviors and assess their implementation readiness for older adult care. Methods We conducted a structured search in the Scopus, Web of Science, and ACM Digital Library databases. Predefined inclusion criteria included studies on unobtrusive sensor-based technology; experimental in nature; aimed at monitoring human social, emotional, physical, and physiological behavior; having the potential to be scalable in in-home care; and having at least 5 adults as participants. Using these criteria, we screened studies by title, abstract, and full text. A deductive thematic analysis based on the Proctor implementation framework along with an additional outcome of external validity was applied to the included studies to identify the factors contributing to successful implementation. Finally, the identified factors were used to report the implementation readiness of the included studies for older adult care. Results In this review, 52 studies were included. Deductive analysis using the implementation framework by Proctor resulted in six factors that can contribute to the successful implementation of USSs in older adult care: study settings, age of participants, activities monitored, sensor setup, sensing technology used, and usefulness of USSs. These factors were associated with the implementation outcomes as follows: study settings and age of participants contributed to external validity, sensor setup contributed to acceptability, usefulness of USSs contributed to adoption, activities monitored contributed to appropriateness, and sensing technology used contributed to implementation cost. Furthermore, the implementation assessment of the included 52 studies showed that none of the studies addressed all the identified factors. This assessment was useful in highlighting studies that have addressed multiple factors; thus, these studies represent a step ahead in the implementation process. Conclusions This review is the first to scope state-of-the-art USSs suitable for older adult care. Although the included 52 USS studies fulfilled the basic criteria to be suitable for older adult care, systems leveraging radio frequency technology in a no-contact sensor setup for monitoring life risk or health wellness activities are more suitable for older adult care. Finally, this review has extended the discussion about unobtrusiveness as a property of systems that cannot be measured in binary because it varies greatly with user perception and context.

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“…However, these techniques are difficult to apply in our setting, as it is unclear that commonly-used transformations would preserve the nature and structure of the data. For instance, a re-scaled step signal may be confused with a fall signal, as the amplitude of the applied force is the main difference between the two signals (see [68]). In our case, we are dealing with a small dataset, which is composed of complex signals, thus making end-to-end learning a difficult task.…”

Section: Methodsmentioning

confidence: 99%

NurseNet: Monitoring Elderly Levels of Activity with a Piezoelectric Floor

Minvielle

Audiffren

2019

Sensors

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Monitoring the activity of elderly individuals in nursing homes is key, as it has been shown that physical activity leads to significant health improvement. In this work, we introduce NurseNet, a system that combines an unobtrusive, affordable, and robust piezoelectric floor sensor with a convolutional neural network algorithm, which aims at measuring elderly physical activity. Our algorithm is trained using signal embedding based on atoms of a pre-learned dictionary and focuses the network’s attention on step-related signals. We show that NurseNet is able to avoid the main limitation of floor sensors by recognizing relevant signals (i.e., signals produced by patients) and ignoring events related to the medical staff, offering a new tool to monitor elderly activity in nursing homes efficiently.

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Fall detection using smart floor sensor and supervised learning

Cited by 21 publications

References 8 publications

Elderly Perception on the Internet of Things-Based Integrated Smart-Home System

Elderly Perception on the Internet of Things-Based Integrated Smart-Home System

Implementation of Unobtrusive Sensing Systems for Older Adult Care: Scoping Review

NurseNet: Monitoring Elderly Levels of Activity with a Piezoelectric Floor

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