Adaptive Body Posture Analysis for Elderly-Falling Detection with Multisensors

Lai, Chin-Feng; Huang, Yueh‐Min; Park, Jong Hyuk; Chao, Han-Chieh

doi:10.1109/mis.2010.39

Cited by 81 publications

(31 citation statements)

References 11 publications

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“…Although this approach is physically less intrusive for the user compared to one based on wearable sensors, it suffers from problems such as target occlusion, timeconsuming processing and privacy concerns. The most mature approach to both AR and FD is probably using wearable accelerometers [12,13,14,15]. There are two common types of wearable-sensor approach that have proven successful: those that use domain knowledge encoded with rules, and those that use machine learning.…”

Section: Related Workmentioning

confidence: 99%

“…Wu et al [13] presented an approach in which decision rules are used to recognize activities. Another implementation of such rules was presented by Lai et al [14], who used six accelerometers, placed on the neck, waist, both wrists and both thighs. The reported accuracy was almost perfect (99.5%), but the number of sensors is excessive for everyday use.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Activity Recognition and Fall Detection Using Accelerometers

Kozina

Gjoreski

Gams

et al. 2013

Communications in Computer and Information Science

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Abstract. Ambient assisted living (AAL) systems need to understand the user's situation, which makes activity recognition an important component. Falls are one of the most critical problems of the elderly, so AAL systems often incorporate fall detection. We present an activity recognition (AR) and fall detection (FD) system aiming to provide robust real-time performance. It uses two wearable accelerometers, since this is probably the most mature technology for such purpose. For the AR, we developed an architecture that combines rules to recognize postures, which ensures that the behavior of the system is predictable and robust, and classifiers trained with machine learning algorithms, which provide maximum accuracy in the cases that cannot be handled by the rules. For the FD, rules are used that take into account high accelerations associated with falls and the recognized horizontal orientation (e.g., falling is often followed by lying). The system was tested on a dataset containing a wide range of activities, two different types of falls and two events easily mistaken for falls. The Fmeasure of the AR was 99 %, even though it was never tested on the same persons it was trained on. The F-measure of the FD was 78 % due to the difficulty of the events to be recognized and the need for real-time performance, which made it impossible to rely on the recognition of long lying after a fall.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Efficient Activity Recognition and Fall Detection Using Accelerometers

Kozina

Gjoreski

Gams

et al. 2013

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…Health care systems are being examined as a very useful space for BASNs. They can allow for remote monitoring of patients [16] [19], fall detection in the elderly [20], and better physiotherapy treatments [21]. Some military applications are being examined [22][23], as well as professional sports training [24], general exercise promotion [25][26] [27], and security authentication and information sharing [2].…”

Section: Body Area Sensor Network Applicationsmentioning

confidence: 99%

Wearable Sensor Networks for Motion Capture

Arsenault¹,

Whitehead²

2015

Proceedings of the 7th International Conference on Intelligent Technologies for Interactive Entertainment

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Abstract-This work presents the development of a full body sensor-based motion tracking system that functions through wearable inertial sensors. The system is comprised of a total of ten wearable sensors and maps the player's motions to an onscreen character in real-time. A hierarchical skeletal model was implemented that allows players to navigate and interact with the virtual world without the need of a hand-held controller. To demonstrate the capabilities of the system, a simple virtual reality game was created. As a wearable system, the ability for the users to engage in activities while not being tied to a camera system, or being forced indoors presents a significant opportunity for mobile entertainment, augmented reality and interactive systems that use the body as a significant form of input. This paper outlines the key developments necessary to implement such a system.

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“…Studies on smart home [3][4][5][6] such as fire monitoring and detection have been researched for many years. Most monitoring and detection approaches make use of temperature and humidity sensors, smoke detectors, cameras and so on.…”

Section: Related Workmentioning

confidence: 99%

A Novel Mechanism for Fire Detection in Subway Transportation Systems Based on Wireless Sensor Networks

Zhang

Jun

Chiang

et al. 2013

International Journal of Distributed Sensor Networks

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Fire is a common and disastrous phenomenon in subway transportation systems because of closed environment and large passenger flow. Traditional methods detect and forecast fire incidents by fusing the data collected by wireless sensor networks and compare the fusion result with a threshold. However, this approach has a significant shortcoming. Fusion of data decreases the amount of useful information since the distribution of data is not considered. In this paper, a novel mechanism based on wireless sensor networks is proposed and we can use the mechanism to detect fire in subway transportation systems. The core algorithm in this mechanism is an alternative of data fusion in wireless sensor networks. The mechanism uses the quantity information acquired from the data and also considers the distribution information of the data in order to predict the occurrence of fire incidents. The simulation results show that fire incidents can be detected fast and efficiently.

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Adaptive Body Posture Analysis for Elderly-Falling Detection with Multisensors

Cited by 81 publications

References 11 publications

Efficient Activity Recognition and Fall Detection Using Accelerometers

Efficient Activity Recognition and Fall Detection Using Accelerometers

Wearable Sensor Networks for Motion Capture

A Novel Mechanism for Fire Detection in Subway Transportation Systems Based on Wireless Sensor Networks

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