Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

Koshmak, Gregory; Loutfi, Amy; Lindén, María

doi:10.1155/2016/6931789

Cited by 69 publications

(47 citation statements)

References 55 publications

(73 reference statements)

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“…A true negative (TN) is when the system accurately detects that there was no risk situation, while a false negative (FN) is an incorrect negative prediction (Belshaw et al, 2012). SHE prototypes can be evaluated in a realistic environmental setting with a participant performing actual tasks (Koshmak, Loutfi, & Lindén, 2015). Each scenario comprises tasks that can be carefully modelled to depict an envisioned real life situation, i.e.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

Chiridza¹,

Wesson²,

Vogts

2019

SACJ

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“…In addition to combining different algorithms using the same sensor, also improvement in performance can be found in combining several types of sensors. This has currently received a lot of attention, but it has its own challenges and issues as described in [51].…”

Section: Future Workmentioning

confidence: 99%

Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

et al. 2017

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More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. Camera-based fall detection systems can help by triggering an alarm when falls occur. Previously we showed that real-life data poses significant challenges, resulting in high false alarm rates. Here, we show three ways to tackle this. First, using a particle filter combined with a person detector increases the robustness of our foreground segmentation, reducing the number of false alarms by 50%. Second, selecting only nonoccluded falls for training further decreases the false alarm rate on average from 31.4 to 26 falls per day. But, most importantly, this improvement is also shown by the doubling of the AUC of the precision-recall curve compared to using all falls. Third, personalizing the detector by adding several days containing only normal activities, no fall incidents, of the monitored person to the training data further increases the robustness of our fall detection system. In one case, this reduced the number of false alarms by a factor of 7 while in another one the sensitivity increased by 17% for an increase of the false alarms of 11%.

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“…These include wearable devices such as data and privacy of the patients, which may not be an issue for wearable sensors [16]. Wearables sensors, however, require users' cooperation and compliance to be worn or carried, which could be potentially problematic and uncomfortable as highlighted in the introduction of [15].…”

Section: Introductionmentioning

confidence: 99%

“…Additional discussion on gaps and complementarity of radar and RGB-D sensors is provided later on in this paper. Interesting examples of multi-sensing systems are provided in [16], [17], for example Kinect and accelerometers, or cameras with microphones plus accelerometers, but examples of radar and RGB-D systems are not mentioned in those review papers.…”

Section: Introductionmentioning

confidence: 99%

Radar and RGB-Depth Sensors for Fall Detection: A Review

et al. 2017

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Abstract-This paper reviews recent works in the literature on the use of systems based on Radar and RGB-Depth sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users' acceptance and compliance, compared with other sensor technologies such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.

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Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

Cited by 69 publications

References 55 publications

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

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

Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

Radar and RGB-Depth Sensors for Fall Detection: A Review

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