Real-time low-energy fall detection algorithm with a Programmable Truncated MAC

Solaz, Manuel de la Guia; Bourke, Alan K.; Conway, Richard; Nelson, John; ÓLaighin, Gearóid

doi:10.1109/iembs.2010.5626244

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…From the conducted review of the literature, it emerges that there are mainly two lines of research: (a) studies concerning the comparison between old versus young subjects [ 42 , 65 , 71 , 84 ] and (b) works aiming at underlining some peculiar differences between groups of elderly fallers and non-fallers [ 43 , 79 , 80 , 81 ]. In both cases, there is a prevalent presence of healthy elderly groups.…”

Section: Discussionmentioning

confidence: 99%

Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review

Rucco

Sorriso

Liparoti

et al. 2018

Sensors

113

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In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it. Wearable sensors have proved to be very useful in monitoring and analyzing the stability of subjects. Within this manuscript, a review of the approaches proposed in the literature for fall risk assessment, fall prevention and fall detection in healthy elderly is provided. The review has been carried out by using the most adopted publication databases and by defining a search strategy based on keywords and boolean algebra constructs. The analysis aims at evaluating the state of the art of such kind of monitoring, both in terms of most adopted sensor technologies and of their location on the human body. The review has been extended to both dynamic and static analyses. In order to provide a useful tool for researchers involved in this field, the manuscript also focuses on the tests conducted in the analyzed studies, mainly in terms of characteristics of the population involved and of the tasks used. Finally, the main trends related to sensor typology, sensor location and tasks have been identified.

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Section: Discussionmentioning

confidence: 99%

Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review

Rucco

Sorriso

Liparoti

et al. 2018

Sensors

113

View full text Add to dashboard Cite

show abstract

“…The complexity of the algorithm designed and implemented in this research process the data at real time and the computations are quick too. Although the accuracy of the design in [2] is near 100% which is better than the presented algorithm. Also the power saved is higher as the system does not use battery powered monitoring devices.…”

Section: Comparison With Present Algorithmsmentioning

confidence: 76%

“…Therefore, the scalability of algorithm is higher. Also seen in [2], [18] algorithm uses a truncated multiplier to set the threshold limits. This makes the algorithm extremely complex in terms of computations as it involves filtering data, multiplication and squaring.…”

Section: Comparison With Present Algorithmsmentioning

confidence: 99%

“…[1] Approximately 3% of all fallers lie for more than 20 minutes without external support. [2] The need of assistance in the case of unconsciousness or extreme injury is the main reasons why elders leave the comfort and privacy of their own home to live in an assisted-care environment (40% of nursing home admissions are due to falls). [1] Thirty-two percent of elderly people aged over 75 years have ever fallen at least once a year, and among them, 24% have seriously injured.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reliable and Secure Body fall Detection Algorithm in a Wireless Mesh Network

Rakhecha¹,

Hsu²

2013

Proceedings of the 8th International Conference on Body Area Networks

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Foremost, I would like to express my sincere gratitude to my primary thesis advisor Dr. Kenneth Hsu for his patience, motivation and constant support and guidance that he extended throughout the duration of this research. His valuable suggestions and useful comments, this work would not have been possible. This thesis owes much of its contents to his ideas and guidance. My sincere thanks to Dr. Amlan Ganguly and Dr. Andres Kwasinski, whose constructive comments and suggestions improved the quality of this dissertation. Special thanks to Sam Skalicky who helped me with coding throughout the research. I have no words to express my gratitude to my family for their endless support and love and my friends for their continued support and encouragement.

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“…Therefore, the energy efficiency evaluation using the current of Asgard under daily running can be considered as the current consumption of Asgard. According to the analysis above, we can save 9.13% energy while maintaining high accuracy of fall detection, saving more overall system energy than noted in research by (de la Guia Solaz et al, 2010). If Asgard is powered by two AA batteries with a capacity of 1500 mAH, it can be continuously running for about 1500/2.63 = 1141 h, which are 96 more hours than a fall detector running without SSR-based strategy but with the same detection accuracy.…”

Section: Performance Evaluation Of Fall Detection Based On Ssr Energymentioning

confidence: 94%

Real-time energy-efficient fall detection based on SSR energy efficiency strategy

Ren

Shi

Yu³

et al. 2016

IJSNET

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Falling of the elderly has become a growing concern of the community due to the increase of the ageing population and the serious consequences caused by falling. Devising a fall detection system that is not only highly accurate and reliable, but energy efficient and durable is a challenge. In this paper, we proposed an energy efficient fall detection algorithm based on segmented sampling rates. Most of the time, the algorithm uses a low sampling rate to minimise the energy consumption, but a higher sampling rate when a possible fall is sensed. This unique design helps to increase the fall detection accuracy, while reducing the total energy consumption. Results of comprehensive performance evaluation show that the accuracy rate of the proposed fall detection algorithm is 98.33%, meanwhile, the system can save energy by 9.13% comparing to other algorithms running with a high sampling rate without an energy efficient strategy.

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Real-time low-energy fall detection algorithm with a Programmable Truncated MAC

Cited by 9 publications

References 17 publications

Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review

Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review

Reliable and Secure Body fall Detection Algorithm in a Wireless Mesh Network

Real-time energy-efficient fall detection based on SSR energy efficiency strategy

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