Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model

Lim, DongHa; Park, Chulho; Kim, Nam Ho; Kim, Sang-Hoon; Yu, Yun Seop

doi:10.1155/2014/896030

Cited by 62 publications

(28 citation statements)

References 29 publications

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“…Parallel direct tracking (and observation as far as possible) or video footage recorded using camera traps could be used to expand upon training data sets and further assess the accuracy of these methods with respect to inter-individual variation and terrain. Classification improvements may be made by experimenting with additional algorithms such as hidden Markov models (for example, [40]). Even on the second night post-release, it is possible that the badger's behaviour was influenced by the trapping event due to lost foraging time.…”

Section: Figure 1 Gps Tracks and Behaviour Of A Free-ranging Adult Fementioning

confidence: 99%

Behaviour-time budget and functional habitat use of a free-ranging European badger(Meles meles)

et al. 2015

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Background:The European badger (Meles meles) is involved in the maintenance of bovine tuberculosis infection and onward spread to cattle. However, little is known about how transmission occurs. One possible route could be through direct contact between infected badgers and cattle. It is also possible that indirect contact between cattle and infected badger excretory products such as faeces or urine may occur either on pasture or within and around farm buildings. A better understanding of behaviour patterns in wild badgers may help to develop biosecurity measures to minimise direct and indirect contact between badgers and cattle. However, monitoring the behaviour of free-ranging badgers can be logistically challenging and labour intensive due to their nocturnal and semi-fossorial nature. We trialled a GPS and tri-axial accelerometer-equipped collar on a free-ranging badger to assess its potential value to elucidate behaviour-time budgets and functional habitat use. Results: During the recording period between 16:00 and 08:00 on a single night, resting was the most commonly identified behaviour (67.4%) followed by walking (20.9%), snuffling (9.5%) and trotting (2.3%). When examining accelerometer data associated with each GPS fix and habitat type (occurring 2 min 30 s before and after), walking was the most common behaviour in woodland (40.3%) and arable habitats (53.8%), while snuffling was the most common behaviour in pasture (61.9%). Several nocturnal resting periods were also observed. The total distance travelled was 2.28 km. Conclusions:In the present report, we demonstrate proof of principle in the application of a combined GPS and accelerometer device to collect detailed quantitative data on wild badger behaviour. Behaviour-time budgets allow us to investigate how badgers allocate energy to different activities and how this might change with disease status. Such information could be useful in the development of measures to reduce opportunities for onward transmission of bovine tuberculosis from badgers to cattle.

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Section: Figure 1 Gps Tracks and Behaviour Of A Free-ranging Adult Fementioning

confidence: 99%

Behaviour-time budget and functional habitat use of a free-ranging European badger(Meles meles)

et al. 2015

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“…In comparison to the evaluation results from similar work, the SHE prototype was able to accurately and consistently support safety and risk monitoring in accordance with the requirements of the elderly living independently. Fall detection systems and risk monitoring systems notification systems are not 100% accurate, due to various limitations, which result in false positives (El-Bendary, Tan, Pivot, & Lam, 2013) In an experimental evaluation of an acoustic-based fall detection system consisting of 120 falls and non-falls, a 97.50% fall detection rate was recorded with a 3% false detection (Lim, Park, Kim, Kim, & Yu, 2014). A computer vision based algorithm was evaluated and had a correct detection rate of 84.44% (El-Bendary et al, 2013).…”

Section: Resultsmentioning

confidence: 99%

A Smart Home Environment to Support Risk Monitoring for the Elderly Living Independently

Chiridza¹,

Wesson²,

Vogts

2019

SACJ

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Elderly people prefer to live independently despite being vulnerable to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks, and inactivity due to unconsciousness. The aim of this paper is to discuss the development of a low-cost Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE prototype that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was designed and implemented. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. The results show that the prototype has a mean accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%.

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“…It is employed to quantify the ability of the proposed device to detect falling and is defined in Equation (6) [16]. Similarly, specificity, sometimes called the "true negative rate", is the ratio of correctly classified negative statuses to the entire set of negative statuses [63]. It is expressed in Equation (7) [16]:…”

Section: Performance Validation Of the Accelerometer Measurementsmentioning

confidence: 99%

An Autonomous Wireless Health Monitoring System Based on Heartbeat and Accelerometer Sensors

Fakhrulddin

Gharghan

2019

JSAN

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Falls are a main cause of injury for patients with certain diseases. Patients who wear health monitoring systems can go about daily activities without limitations, thereby enhancing their quality of life. In this paper, patient falls and heart rate were accurately detected and measured using two proposed algorithms. The first algorithm, abnormal heart rate detection (AHRD), improves patient heart rate measurement accuracy and distinguishes between normal and abnormal heart rate functions. The second algorithm, TB-AIC, combines an acceleration threshold and monitoring of patient activity/inactivity functions to accurately detect patient falls. The two algorithms were practically implemented in a proposed autonomous wireless health monitoring system (AWHMS). The AWHMS was implemented based on a GSM module, GPS, microcontroller, heartbeat and accelerometer sensors, and a smartphone. The measurement accuracy of the recorded heart rate was evaluated based on the mean absolute error, Bland–Altman plots, and correlation coefficients. Fourteen types of patient activities were considered (seven types of falling and seven types of daily activities) to determine the fall detection accuracy. The results indicate that the proposed AWHMS succeeded in monitoring the patient’s vital signs, with heart rate measurement and fall detection accuracies of 98.75% and 99.11%, respectively. In addition, the sensitivity and specificity of the fall detection algorithm (both 99.12%) were explored.

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Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model

Cited by 62 publications

References 29 publications

Behaviour-time budget and functional habitat use of a free-ranging European badger(Meles meles)

Behaviour-time budget and functional habitat use of a free-ranging European badger(Meles meles)

A Smart Home Environment to Support Risk Monitoring for the Elderly Living Independently

An Autonomous Wireless Health Monitoring System Based on Heartbeat and Accelerometer Sensors

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