Promoting Data Availability Framework by Using Gamification on Smartphone Fall Detection Based Human Activities

Astriani, Maria Seraphina; Bahana, Raymond; Kurniawan, Andreas; Yi, Lee Huey

doi:10.1016/j.procs.2021.01.100

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wearable techniques are mainly based on gyroscopes, accelerometers or an Inertial Measurement Unit (IMU) [ 6 , 7 , 8 ]. These sensors are embedded in various products, such as belts, watches, necklaces, rings, shoes, bracelets, or wristbands [ 9 , 10 , 11 , 12 , 13 ]. According to the changes of movement characteristics, different fall events can be recognized.…”

Section: Introductionmentioning

confidence: 99%

A Non-Contact Fall Detection Method for Bathroom Application Based on MEMS Infrared Sensors

Liu

Zhong

et al. 2023

Micromachines

View full text Add to dashboard Cite

The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and F1-Score are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.

show abstract

Section: Introductionmentioning

confidence: 99%

A Non-Contact Fall Detection Method for Bathroom Application Based on MEMS Infrared Sensors

Liu

Zhong

et al. 2023

Micromachines

View full text Add to dashboard Cite

show abstract

“…At present, there are also studies that implement the fall detection algorithm in the edge computing platform and integrate it into the smart home environment [19][20][21]. The method proposed in [22] deploys the network model to the edge computing platform, reports fall events through WiFi, and performs fall detection while protecting user privacy.…”

Section: Introductionmentioning

confidence: 99%

Fall detection based on OpenPose and MobileNetV2 network

Gao

Zhou

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

The proposed fall detection approach is aimed at building a support system for the elders. In this work, a method based on human pose estimation and lightweight neural network is used to detect falls. First, the OpenPose is used to extract human keypoints and label them in the images. After that, the modified MobileNetV2 network is used to detect falls by integrating both human keypoint information and pose information in the original images. The above operation can use the original image information to correct the deviation in the keypoint labeling process. Through experiments, the accuracy of the proposed method is 98.6% and 99.75% on the UR and Le2i datasets, which is higher than the listed comparison methods.

show abstract

A Fall Detection Algorithm Based on Improved MobileNetV2

Gao

2022

2022 5th International Conference on Mechatronics, Robotics and Automation (ICMRA)

View full text Add to dashboard Cite

Promoting Data Availability Framework by Using Gamification on Smartphone Fall Detection Based Human Activities

Cited by 4 publications

References 11 publications

A Non-Contact Fall Detection Method for Bathroom Application Based on MEMS Infrared Sensors

A Non-Contact Fall Detection Method for Bathroom Application Based on MEMS Infrared Sensors

Fall detection based on OpenPose and MobileNetV2 network

A Fall Detection Algorithm Based on Improved MobileNetV2

Contact Info

Product

Resources

About