Application of k Nearest Neighbors Approach to the fall detection of elderly people using depth-based sensors

Bilski, Piotr; Mazurek, Paweł; Wagner, Jakub

doi:10.1109/idaacs.2015.7341400

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…For RGB-D sensors, the number of subjects is generally around 5 people, but more subjects have been reported in [80] (8 people) and in [101] (11 people), and in particular in [86] (35 people) and [91] (26 people). Accuracy close to 100% is reported in [83], [85], and [87], where a limited amount of sequences, respectively 64, 87 and 36, have been tested. Regarding radar sensors, the reported accuracy rate can vary between approximately 80% up to a claimed 100%, and the corresponding false alarm rates can vary between 20% down to close to 0%.…”

Section: Rgb-d Datasets For Fall Detectionmentioning

confidence: 86%

“…Thirty-five young volunteers were involved in the recording of the dataset used to evaluate the system, collected considering two Kinect sensors. Bilski et al [87] proposed the use of two synchronized Kinect sensors and an algorithm based on kNN to detect falls. The depth frame is initially transformed into the absolute representation based on global space coordinates.…”

Section: A Fall Detection Using Depth Datamentioning

confidence: 99%

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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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Section: Rgb-d Datasets For Fall Detectionmentioning

confidence: 86%

Section: A Fall Detection Using Depth Datamentioning

confidence: 99%

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

et al. 2017

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“…Human Detection is being used in various applications. Human detection constitutes the first phase in a variety of applications for examples "intelligent digital content management" [201]- [203], "driving assistance systems" [204]- [207], "smart video surveillance" [208]- [210], "abnormal behavior" [211], [212], "crowd scene analysis (people counting)" [213], [214], "person re-identification" [215]- [217], "human tacking" [218]- [220], "human activity recognition" [221]- [224], "human pose estimation" [225]- [228], "gender classification" [229]- [232], "pedestrian detection" [233]- [238] and "e-health systems" [239]- [243].…”

Section: Applicationmentioning

confidence: 99%

Vision-Based Human Detection Techniques: A Descriptive Review

Sumit

Mirjalili

2021

IEEE Access

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“…Nevertheless, the performance heavily depends on the fixed threshold level. Hence, it is rarely used alone, and often combined with other machine learning methods such as decision tree (DT) [9], [10], artificial neural networks (ANN) [11], hidden Markov model (HMM) [12] and Support Vector Machine (SVM) [4], [14], [15] can be combined to outperform the threshold method [8], [14]. Among the machine learning methods, SVM was found the most robust for fall detection when compared to other methods such as threshold-based methods and the decision tree method [8].…”

Section: Corresponding Authormentioning

confidence: 99%

Fall Detection using Lifting Wavelet Transform and Support Vector Machine

Liang

Usaha

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Frequency domain features of inertial movement enables multi-resolution analysis for fall detection, yet they are computationally intensive. This paper proposes a computationally light frequency domain feature extraction method based on lifting wavelet transform (LWT) which provides computational efficiency suitable for real-time low power devices such as wearable sensors for human fall detection. LWT is combined with support vector machine (SVM) to identify falls from activities of daily living. Performance of the Haar and Biorthogonal 2.2 wavelets were compared with the time domain feature of root-mean square acceleration using a human fall dataset. Results show that the first level detail coefficients features for both Haar and Biorthogonal 2.2 wavelets achieve good overall fall detection accuracy, sensitivity and specificity.

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Application of k Nearest Neighbors Approach to the fall detection of elderly people using depth-based sensors

Cited by 9 publications

References 11 publications

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

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

Vision-Based Human Detection Techniques: A Descriptive Review

Fall Detection using Lifting Wavelet Transform and Support Vector Machine

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