A fall detection and near-fall data collection system

Dinh, Anh; Teng, Daniel; Chen, Li; Shi, Yang; McCrosky, Carl; Basran, Jenny; Bello-Hass, V. Del; Ko, Seok‐Bum; Ralhan, A.; Williams, David E.; Windels, N.; Choudhury, Askar H.

doi:10.1109/mnrc.2008.4683392

Cited by 2 publications

(1 citation statement)

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“…Such methods infer a fall by incorporating lumbar sensors to capture the elderly population's daily activity [21]. Wearable sensors can provide information to specific models to collect and analyze data streams and infer a fall detection incident [22]. A wearable biomedical signal measurement terminal is also able to perform automatic fall detection by feeding specific inference models [23].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Kyriakopoulos

Ntanos

Anagnostopoulos

et al. 2020

IJERPH

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Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

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Section: Introductionmentioning

confidence: 99%

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Kyriakopoulos

Ntanos

Anagnostopoulos

et al. 2020

IJERPH

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Optimization procedure for the impact detection thresholds in an Accelerometer Smart Sensor

Naranjo

Roa

Reina‐Tosina

et al. 2009

2009 9th International Conference on Information Technology and Applications in Biomedicine

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The main objective of this work is to perform an optimization methodology of the temporal parameters and operation thresholds (acceleration and energy) for the impact detection in an Accelerometer Smart Sensor. The procedure is based on a sequential approach to values that improve the sensitivity and the specificity of the impact detection. It was fed in its execution with a set of experiments of different daily activities, with and without impact. The values of the parameters obtained from the optimization were tested with a different set of experiments achieving a 100% success (zero false positives and zero false negatives), confirming the robustness of the procedure.

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A fall detection and near-fall data collection system

Cited by 2 publications

References 10 publications

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Optimization procedure for the impact detection thresholds in an Accelerometer Smart Sensor

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