A Lightweight Neural-Net with Assistive Mobile Robot for Human Fall Detection System

Chin, Wei Hong; Tay, Noel Nuo Wi; Kubota, Naoyuki; Loo, Chu Kiong

doi:10.1109/ijcnn48605.2020.9206637

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…The performance of these algorithms is between 93% and 97.4%. Regarding the speed, one has five FPS [ 81 ] and another 24 FPS [ 75 ]. The rest runs in real time.…”

Section: Discussion and Conclusionmentioning

confidence: 99%

“…The rest runs in real time. In terms of computer resources, a computer with advanced hardware is used in [ 76 ], as well as robotic platforms such as MOBOT [ 77 ], Hobbit [ 80 ], Roomba [ 81 ], and a customized robot [ 78 ].…”

Section: Discussion and Conclusionmentioning

confidence: 99%

“…In addition, Chin et al in [ 81 ] developed an algorithm for a mobile robot to detect falls in older adults living alone at home. First, spatial and temporal information is computed using a dense optical flow (DOF) algorithm.…”

Section: Algorithms Used For the Bodymentioning

confidence: 99%

See 2 more Smart Citations

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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“…The performance of these algorithms is between 93% and 97.4%. Regarding the speed, one has five FPS [ 81 ] and another 24 FPS [ 75 ]. The rest runs in real time.…”

Section: Discussion and Conclusionmentioning

confidence: 99%

Section: Discussion and Conclusionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…• To address this problem, efficient resource consumption is required. This can be solved by finding a balance between computational resources, and performance improvement [53]- [55].…”

Section: Related Workmentioning

confidence: 99%

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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Robotic Healthcare Companion for the Elderly and the Differently Abled with Indoor Human Following and Fall Detection Capability

Seneviratne,

De Zoysa,

Senarathna

et al. 2024

2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)

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A Lightweight Neural-Net with Assistive Mobile Robot for Human Fall Detection System

Cited by 3 publications

References 21 publications

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Activity Classification and Fall Detection Using Monocular Depth and Motion Analysis

Robotic Healthcare Companion for the Elderly and the Differently Abled with Indoor Human Following and Fall Detection Capability

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