Internet of Things (IoT) Fall Detection using Wearable Sensor

Yee, Loh Mei; Chin, Lim Chee; Fook, Chong Yen; Dali, Maslia Binti; Basah, Shafriza Nisha; Chee, Lim Sin

doi:10.1088/1742-6596/1372/1/012048

Cited by 14 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One study detailed a fall-detection system that provides a centralized system through a mobile application based on the cloud to gather data on all monitored persons [36]. In 2019, Yee et al developed a wearable, sensor-based fall-prevention device that can differentiate between falling and non-falling cases with the help of a k-NN classifier [37]. In another 2019 study, Khan et al proposed a wearable device consisting of a camera, gyroscope, and accelerometer that remotely detects patient falls [38].…”

Section: Internet Of Things and Patient Fallsmentioning

confidence: 99%

Internet of things in healthcare for patient safety: an empirical study

Yesmin

Carter

Gladman³

2022

BMC Health Serv Res

View full text Add to dashboard Cite

Introduction This study evaluates the impact of an Internet of Things (IoT) intervention in a hospital unit and provides empirical evidence on the effects of smart technologies on patient safety (patient falls and hand hygiene compliance rate) and staff experiences. Method We have conducted a post-intervention analysis of hand hygiene (HH) compliance rate, and a pre-and post-intervention interrupted time-series (ITS) analysis of the patient falls rates. Lastly, we investigated staff experiences by conducting semi-structured open-ended interviews based on Roger’s Diffusion of Innovation Theory. Results The results showed that (i) there was no statistically significant change in the mean patient fall rates. ITS analysis revealed non-significant incremental changes in mean patient falls (− 0.14 falls/quarter/1000 patient-days). (ii) HH compliance rates were observed to increase in the first year then decrease in the second year for all staff types and room types. (iii) qualitative interviews with the nurses reported improvement in direct patient care time, and a reduced number of patient falls. Conclusion This study provides empirical evidence of some positive changes in the outcome variables of interest and the interviews with the staff of that unit reported similar results as well. Notably, our observations identified behavioral and environmental issues as being particularly important for ensuring success during an IoT innovation implementation within a hospital setting.

show abstract

Section: Internet Of Things and Patient Fallsmentioning

confidence: 99%

Internet of things in healthcare for patient safety: an empirical study

Yesmin

Carter

Gladman³

2022

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

“…In any case, commercial off-the-shelf motes employed for these prototypes only embed short-range low-power connection technologies, so an external gateway must be located in the close vicinity of the mote to receive the alarms, which reduces the freedom of movements of the monitored user. In the studies where the mote is provided with a wireless interface with a higher transmission range, such as Wi-Fi in [63,64], GSM in [65][66][67][68] or both GSM and wi-fi [69], the battery lifetime of the system is not usually evaluated.…”

Section: Consumption In Hybrid Fall Detection Architectures (Combininmentioning

confidence: 99%

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

González-Cañete

Casilari

2020

Sensors

View full text Add to dashboard Cite

Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.

show abstract

“…As a second stage, in order to refine the fall detection accuracy, different neural-network classifiers can be implemented on the machine learning module of the wearable device, trained with specific fall datasets [40]. Recent studies, implementing Radial Basis Function (RBF) on SVM [41,42] and k-NN [43] on low power devices, demonstrate that the adoption of machine learning algorithms allows to achieve an accuracy higher than 95%. Furthermore, applicability of machine learning algorithms can be expanded with the adoption of more performing processors, enabling the adoption of TensorFlow libraries.…”

Section: Fall Detection Algorithmmentioning

confidence: 99%

“…Concerning other machine learning algorithms, k-NN classifier has also been tested in [43], on a slightly different wearable device (i.e., Arduino Mega), providing more than 99% sensitivity, accuracy, and specificity. However, with respect to our device, the Arduino Mega adopted in [43] has not been optimized with pattern recognition acceleration for the adoption of k-NN. Therefore, we can reasonably assume that our device can achieve at least the same satisfactory performance (more than 99%).…”

Section: Testing and Validation Of The Architecturementioning

confidence: 99%

An IoT Smart Environment in Support of Disease Diagnosis Decentralization

Andreadis

Zambon

2020

Electronics

View full text Add to dashboard Cite

The percentage of seniors in the global population is constantly growing and solutions in the field of fall detection and early detection of neuro-degenerative pathologies have a crucial role in order to increase life expectancy and quality of life. This study aims to extend fall detection and effective recognition of early signs of diseases to new smart environments, conceiving the decentralization of diagnostic monitoring in everyday life activities in a more pervasive paradigm. Inspiring to research outcomes, in this work an architecture is designed to detect falls in crowded indoor environments during events/exhibitions, for favoring a timely and effective intervention. It also foresees a continue monitoring of the gait for seniors during the visit, thus extracting key features which are stored on a dedicated database. The proposed solution allows third party researchers to perform analysis on the obtained gait datasets, through the adoption of advanced data-mining techniques for the detection of early signs of neuro-degenerative diseases and other pathologies. The architecture designed here aims to provide a step forward concerning the extension of smart monitoring environments for the detection of falls and early signs of pathologies in everyday life, in a more pervasive and decentralized paradigm.

show abstract

Internet of Things (IoT) Fall Detection using Wearable Sensor

Cited by 14 publications

References 6 publications

Internet of things in healthcare for patient safety: an empirical study

Internet of things in healthcare for patient safety: an empirical study

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

An IoT Smart Environment in Support of Disease Diagnosis Decentralization

Contact Info

Product

Resources

About