Smart watch RSSI localization and refinement for behavioral classification using laser-SLAM for mapping and fingerprinting

Carlson, Jay D.; Mittek, Mateusz; Parkison, Steven A.; Sathler, Pedro; Bayne, David; Psota, Eric T.; Pérez, Lance C.; Bonasera, Stephen J.

doi:10.1109/embc.2014.6944048

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…The literature shows that the most common use of smartwatches in health care is focused on health monitoring (Carlson et al, 2014;Casilari & Oviedo-Jiménez, 2015;Lee et al, 2015;Mortazavi et al, 2015), and another major application is medication adherence monitoring (Maglogiannis, Spyroglou, Panagopoulos, & Mazonaki, 2014;Sailer, Pobiruchin, & Wiesner, 2015). Although the application of smartwatches in health care is still in the early phase of development, the literature shows great promise, especially in communication and improvement of workflow efficiency (Årsand, Muzny, Bradway, Muzik, & Hartvigsen, 2015;Costas, McCallum, & Landmark, 2015;Scher, 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluating a smartwatch notification system in a simulated nursing home

Ali

2019

Int J Older People Nursing

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Aims This paper evaluates a high‐fidelity prototype of a smartwatch‐based communication system for nursing homes. The system could improve communication between residents and caregivers, thus reducing staff response time and improving residents' safety. Background Across the country, nursing homes are often cited as having serious quality and safety issues. Deficiencies in the communication systems that connect residents with caregivers significantly impact the quality of care. Most nursing homes still use traditional call light systems, that give visual and auditory cues to caregivers, who may or may not be in proximity to the system. This study evaluates a smartwatch communication system developed to quickly relay a resident's requests for assistance to staff anywhere in the nursing home. Method Certified Nursing Assistants (CNAs) employed by nursing homes were recruited to perform routine tasks using both a traditional system and a smart system. Response time to call lights, staff performance and outcomes were measured through direct and recorded observations and through the surveys of participants. Results The smartwatch system reduced staff response time to call lights from bedrooms by 40%, from bathrooms by 58% and from bed exit alarms by 29%. The smartwatch system also reduced perceived workload by about 50% and was highly accepted by the CNAs. Conclusion A smartwatch‐based notification system may increase staff performance and decrease workload, thus leading to greater staff and patient satisfaction, which could ultimately lead to better quality of care and patient safety. Relevance to clinical practice This study provides empirical evidence for the usefulness of smartwatches in facilitating the notification and communication processes in healthcare settings and is also among the first to actually examine a smartwatch system in a healthcare setting.

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Section: Introductionmentioning

confidence: 99%

Evaluating a smartwatch notification system in a simulated nursing home

Ali

2019

Int J Older People Nursing

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“…This work employs a combination of two techniques: fingerprinting and kernel density estimation. 14 The fingerprinting calibration process for WSN is as follows:…”

Section: Device Calibration and Trajectory Extractionmentioning

confidence: 99%

Behavior Analysis through Multimodal Sensing for Care of Parkinson’s and Alzheimer’s Patients

Álvarez¹,

Popa

Solachidis

et al. 2018

IEEE MultiMedia

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The analysis of multimodal data collected by innovative imaging sensors, Internet of Things devices, and user interactions can provide smart and automatic distant monitoring of Parkinson's and Alzheimer's patients and reveal valuable insights for early detection and/or prevention of events related to their health. This article describes a novel system that involves data capturing and multimodal fusion to extract relevant features, analyze data, and provide useful recommendations. The system gathers signals from diverse sources in health monitoring environments, understands the user behavior and context, and triggers proper actions for improving the patient's quality of life. The system offers a multimodal, multi-patient, versatile approach not present in current developments. It also offers comparable or improved results for detection of abnormal behavior in daily motion. The system was implemented and tested during 10 weeks in real environments involving 18 patients.

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“…Bu çalışmadan itibaren iç mekân konum tespiti büyük bir araştırma alanı olarak görülmüş ve büyük firmaların bu konudaki yatırımlarıyla Yıllık Bileşik Büyüme Oranı (YBBO) %44,87 ye varan bir pazara dönüşmüştür [2]. Ekahau [3] ve AeroScout [4] gibi birçok büyük firma bu alanda çalışmalarına devam etmektedir ve araştırmacılar daha düşük maliyetli çözümler üzerinde de çalışmaktadır [5].…”

Section: İlgi̇li̇ çAlişmalar (Related Work)unclassified