IoT-Based Fall Detection for Smart Home Environments

Greene, Stan A.; Thapliyal, Himanshu; Carpenter, David M.

doi:10.1109/inis.2016.017

Cited by 38 publications

(21 citation statements)

References 8 publications

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“…The incorporation of the voice-based features helps in reducing the burden of the learning curve of new technologies on family and caregivers, SN Computer Science thereby improving the quality of life. A similar scheme is proposed by authors in [22], where they use a voice-assistant along with a camera system for fall detection in a smarthome environment. Similar such works have been done by other authors in [23][24][25] for improving the various technical aspects relevant in voice-based systems.…”

Section: Current State-of-art Of Voice-assistantsmentioning

confidence: 99%

Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa

2021

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Section: Current State-of-art Of Voice-assistantsmentioning

confidence: 99%

Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa

2021

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“…In early 1970, Hausnotruf designed the first FDS which was known earlier as the Personal Emergency Response System (PERS). 13 PERS system was developed as a home alert system. The first prototype model of an FDS was developed in 1985.…”

Section: Survey Of Existing Literature Reviewmentioning

confidence: 99%

A systematic review on machine learning for fall detection system

Rastogi

Singh

2021

Computational Intelligence

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Fall is a major threat to the health and life of the elders. A Fall Detection System (FDS) assist the elders by identifying the fall and save their life. Machine Learning-(ML) based FDS has turned into a major research area due to its capability to assist the elders automatically. The efficiency of a FDS depends on its strength to identify the fall from nonfall accurately. The initial fall detection scheme depends on the threshold-based classification to classify the fall from the Activity of Daily Living (ADL) but this classification method has failed to reduce the false alarm rate, which raises a question on the efficiency of the technique. This review work identifies the problems in threshold-based classification from existing works and finds the need for an efficient ML-based classification technique to accurately identify the fall. Then, presents a comprehensive literature review on various ML-based classification in fall detection. Moreover, the scrutiny investigates the shortcomings associated with the ML-based techniques for future research. This study finds that present ML-based FDS has not addressed problems like data preprocessing and data dimensionality reduction techniques even though ML-based techniques are far superior to threshold-based techniques.The study concludes that Self-Adaptive-based FDS, as

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“…A directed acyclic graph support vector machine has been used in Yu et al (2012), in addition to rule-based algorithms and background subtraction algorithms. Greene et al (2016) present an Internet of Things-based fall detection system. This system uses a fall detector; an amazon echo device, a speaker and a webcam (Logitech c920) for this function.…”

Section: Fall Detection and Mobility Related Disease Monitoring Systemsmentioning

confidence: 99%