A 3-phase threshold algorithm for smartphone-based fall detection

Chaitep, Theepop; Chawachat, Jakarin

doi:10.1109/ecticon.2017.8096203

Cited by 12 publications

(13 citation statements)

References 5 publications

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“…Table 5 illustrates examples from these reports. Some reports utilized micro-electro-mechanical systems 31 (MEMS) or accelerometer only, 27 , 30 while others used the accelerometer being integrated to gyroscope with/without magnetometer. 26 , 29 , 33 Some researchers preferred to collaborate the accelerometer with Heart rate Variability (HRV) sensor from the ECG signal (ie, stress indicator) 28 or infra-red sensor (ie, position indicator).…”

Section: Discussionmentioning

confidence: 99%

“…The sensor was stationed at either head, 32 chest, 26 , 32 thigh, 26 , 32 waist, 27 , 29 , 31 , 32 shoulder, 29 wrist, 32 or foot. 29 Some researchers preferred the smart phone style so the fall detection device can be put in the pocket of pants, 30 while other researchers preferred to design a dedicated vest. 33 Table 5 demonstrates that there is no evidence of agreement between researchers groups.…”

Section: Discussionmentioning

confidence: 99%

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A New Prototype of Smart Wearable Monitoring System Solution for Alzheimer’s Patients

Al-Naami¹,

Owida²,

Mallouh³

et al. 2021

MDER

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Purpose The daily life management of patients with Alzheimer’s disease (AD) constitutes a significant and rapidly expanding health-care responsibility. In this study, an innovative prototype of a wireless-sensing smart wearable medical device (SWMD) is proposed as a multi-functions solution for Alzheimer patients. The SWMD is aimed to assemble three main biomedical engineering advances: 1) use of a Wi-Fi microcontroller, 2) simultaneous monitoring of a set of vital biomarkers, and 3) cautions of fall down conditions, in addition to GPS location indicator. Methods The SWMD employs a Wi-Fi controller that is incorporated with electronic circuits to monitor three vital signals (temperature, heart rate, and oxygen saturation), fall down conditions in three directions (X, Y, and Z axis), and GPS location. The SWMD was connected to the Firebase Service (database hosted on the Internet Cloud). The proposed device was tested on 13 normal volunteers. The left side, right side, forward, and backward fall down conditions were assessed. The prototype’s functions during daily activity such as rising hand, sitting down or standing up, and walking conditions were also assessed. Results The three assembled functions were all successfully incorporated to build the SWDM device as a suggested solution offering real-time alerts during daily activity to AD patients. The Bland-Altman statistical test showed no significant difference (p-value >0.05) between the SWMD biomarkers’ acquisition and the reference methods. The gyro/accelerator sensor yielded 93% sensitivity in fall down detection and 95% specificity during daily activities. The GPS yielded correct positioning of the SWDM holder, while the internet cloud allowed saving and managing all vital biomarkers daily. Conclusion The SWMD is a possible solution for daily life support for AD patients. It incorporates three functions in one single device, GPS location indicator, monitoring set of biomarkers, and fall down alert, which are all controlled via a Wi-Fi micro controller on-line connected to Internet Cloud. It successfully would allow the management of the daily records as well as the real-time alerts to remote persons.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A New Prototype of Smart Wearable Monitoring System Solution for Alzheimer’s Patients

Al-Naami¹,

Owida²,

Mallouh³

et al. 2021

MDER

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“…ere is also the possibility of false positives caused by mobile phone drops. Chaitep and Chawachat [27] proposed a threshold-based detection method which makes use of G-force values derived from accelerometer readings to identify falls and smartphone drops. e algorithm consists of 3 phases-checking for a smartphone drop, detecting a fall, and fall confirmation.…”

Section: 1mentioning

confidence: 99%

A Survey on Recent Advances in Wearable Fall Detection Systems

Ramachandran

Karuppiah

2020

BioMed Research International

108

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With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

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“…A wearable sensor-based fall detection system determines their motion status or position information by sensors worn on the older person's body. Most of the researches are based on accelerometers in the study [38][39][40][41][42][43]. The acceleration sensor detection system determines whether or not to fall by analyzing and collecting the acceleration of multiple axes.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…The most common one in the literature is the three-axis accelerometer. For example, Chaitep and Chawachat [41] proposed an algorithm which uses the gravity values derived from accelerometer readings to detect falls in 2017. The method consists of 3 stages: checking for a smartphone drop, detecting a fall, and fall confirmation.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Chen

Wang

et al. 2020

IEEE Access

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Fall event, as one of the greatest risks to the elderly, its detection has been a hot research issue in the solitary scene in recent years. Nevertheless, most current researches are conducted in the ideal environments, without considering the challenge of complex background in real situation. Therefore, this paper aims to detect fall event detection in complex background based on visual data. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method is first used to accurately extract the moving objects in the noise background. Then, an attention guided Bi-directional LSTM model is proposed for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other stateof-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.

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A 3-phase threshold algorithm for smartphone-based fall detection

Cited by 12 publications

References 5 publications

A New Prototype of Smart Wearable Monitoring System Solution for Alzheimer’s Patients

A New Prototype of Smart Wearable Monitoring System Solution for Alzheimer’s Patients

A Survey on Recent Advances in Wearable Fall Detection Systems

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

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