Wrist-worn accelerometer based fall detection for embedded systems and IoT devices using deep learning algorithms

Kraft, Dimitri; Srinivasan, Karthik; Bieber, Gerald

doi:10.1145/3389189.3397983

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…A few literatures have tested their modeling techniques on the UP-Fall Detection Dataset [37,15,11,60,61]. In their approach, [37] demonstrated the efficacy of deep learning approaches over heuristic approaches.…”

Section: Discussionmentioning

confidence: 99%

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ARFDNet: An efficient activity recognition & fall detection system using latent feature pooling

Yadav

Luthra

Tiwari

et al. 2022

Knowledge-Based Systems

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Section: Discussionmentioning

confidence: 99%

“…They scored precision, recall, and f1-score of 95.64%, 95.29%, and 95.44%, respectively. Kraft et al[60] utilized accelerometer and gyroscopes to recognize the activities. Using CNNs with data augmentation and applying quantization techniques, they scored precision, recall, and f1-score of 95.2%, 94.0%, and 94.2%, respectively.…”

mentioning

confidence: 99%

ARFDNet: An efficient activity recognition & fall detection system using latent feature pooling

Yadav

Luthra

Tiwari

et al. 2022

Knowledge-Based Systems

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Systematic literature review of ambient assisted living systems supported by the Internet of Things

Caballero

Ortiz

Medina‐Bulo

2023

Univ Access Inf Soc

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Activity Classification and Fall Detection Using Monocular Depth and Motion Analysis

Mobsite,

Alaoui,

Boulmalf

et al. 2024

IEEE Access

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In the realm of senior care, falls remain the primary cause of hospitalization and mortality. Early detection can facilitate timely medical interventions and mitigate the associated risks. Hence, it is beneficial to set up a mechanism to identify and track such incidents. While wearable sensors are commonly used for this purpose, they may not be feasible for daily use. In contrast, surveillance cameras provide an unobtrusive and convenient monitoring solution. Despite the intricate nature of video frames, employing deep learning techniques for human recognition can enhance the performance of such systems. In our approach, activity classification is triggered only when a person is detected in the environment. We then process the extracted Global History of Motion (GHM) images with our Lightweight Deep Neural Network (LDNN). Additionally, we employ a dilated Convolution Long Short Term Memory (ConvLSTM) coupled with our LDNN to analyze the extracted sequence of monocular depth frames. Ultimately, the decision is made based on the combined information from both streams. Empirical results demonstrate the good performance of our fall detection system on the UP-Fall and UR datasets compared to the state-of-the-art. In terms of person classification, the Xception network achieved an F1-score of 98.46% on a sub-dataset derived from the COCO dataset, and 100% on the UP-Fall dataset.

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Wrist-worn accelerometer based fall detection for embedded systems and IoT devices using deep learning algorithms

Cited by 5 publications

References 29 publications

ARFDNet: An efficient activity recognition & fall detection system using latent feature pooling

ARFDNet: An efficient activity recognition & fall detection system using latent feature pooling

Systematic literature review of ambient assisted living systems supported by the Internet of Things

Activity Classification and Fall Detection Using Monocular Depth and Motion Analysis

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