Fall detection analysis with wearable MEMS-based sensors

Yuan, Xiaocong; Yu, Shiying; Dan, Qiang; Wang, Guoping; Liu, Sheng

doi:10.1109/icept.2015.7236791

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…The aforementioned works coincide in their objective with the Kalman filter (identifying locomotion activities), but they differ in the way it was implemented, and it was never used to detect falls. Other authors have used the Kalman filter as part of their fall detection algorithms [ 25 , 26 , 27 , 28 , 29 ]. All of them used the triaxial accelerometer together with other sensors (a gyroscope in all cases and a magnetometer in one case).…”

Section: Introductionmentioning

confidence: 99%

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

Sucerquia

López

Vargas-Bonilla

2018

Sensors

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The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We tested our approach with the SisFall dataset achieving 99.4% of accuracy. We then validated it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

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Section: Introductionmentioning

confidence: 99%

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

Sucerquia

López

Vargas-Bonilla

2018

Sensors

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“…However, this method is easily affected by the environment, requires more information monitoring equipment, and can only detect seafarer falls in fixed areas [ 10 ], such as indoors. Wearable sensor-based seafarer fall detection methods mainly use specific wearable MEMS devices [ 11 , 12 , 13 ], such as belts and smart watches, to monitor various physical signs of seafarers. If there was an abnormality in various indicators of the body, and the abnormality was consistent with the characteristics of a fall, it was judged as a fall.…”

Section: Related Workmentioning

confidence: 99%

Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Liu

et al. 2022

Sensors

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Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer’s falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose–LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer’s falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors.

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“…Some examples of such sensors are tri-axial accelerometer [9], gyroscope [10], [11], etc. Some wearable devices include three-axis accelerometer, three-axis gyroscope and three-axis magnetometer [12]. These sensors, which has been designed based on the physical devices, detect the change in people's position.…”

Section: A Wearable Sensor and Environmental Sensormentioning

confidence: 99%

Implementation of Fall Detection System Based on 3D Skeleton for Deep Learning Technique

Tsai

Hsu

2019

IEEE Access

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In recent years, the fall detection system has become an important topic in the homecare system. Compared with the traditional fall detection algorithm, the method used by neural network is more robust and has higher accuracy. However neural network consumes a large amount of energy due to a huge number of computations, and needs more memory to store parameters as compared to traditional algorithms. In this paper, we propose a fall detection system in combination of the traditional algorithm with the neural network. First, we propose a skeleton information extraction algorithm, which transforms depth information into skeleton information and extracts the important joints related to fall activity. Also we have modified the skeleton-based method with seven highlight feature points. Second, we propose a highly robust deep convolution neural network architecture, which uses a pruning method to reduce parameters and calculations in the network. The low number of parameters and calculations makes the system suitable for the implementation on an embedded system. The experiment results show the high accuracy and robustness on the popular benchmark dataset NTU RGB+D. The proposed system has been implemented on NVIDIA Jetson Tx2 platform with real-time processing.

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Fall detection analysis with wearable MEMS-based sensors

Cited by 10 publications

References 11 publications

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Implementation of Fall Detection System Based on 3D Skeleton for Deep Learning Technique

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