Fall Detection System for Elderly based on 2D LiDAR: A Preliminary Study of Fall Incident and Activities of Daily Living (ADL) Detection

Miawarni, Herti; Sardjono, Tri Arief; Setijadi, Eko; Wijayanti,; Arraziqi, Dwi; Gumelar, Agustinus Bimo; Purnomo, Mauridhi Hery

doi:10.1109/cenim51130.2020.9298000

Cited by 12 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ogawa et al [24] proposed a fall detection method using infrared radiation sensor array. Miawarni et al [25] proposed 2-dimensional lidar as the main sensor in a fall detection system, with high recognition accuracy.…”

Section: Ambient Sensor-based Fall Detectionmentioning

confidence: 99%

A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

Wang

Yin

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

According to the World Health Organization and other authorities, falls are one of the main causes of accidental injuries among the elderly population. Therefore, it is essential to detect and predict the fall activities of older persons in indoor environments such as homes, nursing, senior residential centers, and care facilities. Due to non-contact and signal confidentiality characteristics, radar equipment is widely used in indoor care, detection, and rescue. This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities. The experimental results show that the method is able to distinguish three types of fall activities (i.e., stand to fall, bow to fall, and squat to fall) and obtain a high recognition accuracy up to 95.7%.

show abstract

Section: Ambient Sensor-based Fall Detectionmentioning

confidence: 99%

A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

Wang

Yin

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Many types of research on fall incident detection systems have been carried out using various methods, tools, and case studies [6]. Specific for this experiment, we used a dataset that collects data in users' daily lives by monitoring movement and falling incidents, using two accelerometer sensors: ADXL345 sensor and the MMA8451q sensor, and one gyroscope sensor: the ITG3200 sensor.…”

Section: Related Workmentioning

confidence: 99%

Towards Improvement of LSTM and SVM Approach for Multiclass Fall Detection System

Miawarni

Setijadi

Sardjono

et al. 2022

EMITTER Int'l J. of Engin. Technol.

Self Cite

View full text Add to dashboard Cite

Telemonitoring of human physiological data helps detect emergency occurrences for subsequent medical diagnosis in daily living environments. One of the fatal emergencies in falling incidents. The goal of this paper is to detect significant incidents such as falls. The fall detection system is essential for human body movement investigation for medical practitioners, researchers, and healthcare businesses. Accelerometers have been presented as a practical, low-cost, and dependable approach for detecting and predicting outpatient movements in the user. The accurate detection of body movements based on accelerometer data enables the creation of more dependable systems for incorporating long-term development in physiological remarks. This research describes an accelerometer-based platform for detecting users' body movement when they fall. The ADXL345, MMA8451q, and ITG3200 body sensors capture activity data, subsequently classified into 15 fall incident classes based on SisFall dataset. Falling incidents classification is performed using Long Short-Term Memory results in best AUC-ROC value of 97.7% and best calculation time of 6.16 seconds. Meanwhile, Support Vector Machines results in the best AUC-ROC value of 98.5% and best calculation times of 17.05 seconds.

show abstract

“…However, their detection accuracy is not very high, and if the elderly do not wear a watch, for example, it is not very convenient to use a walker to go to the toilet after getting up in the morning. Herti et al [22] used LiDAR fixed indoors to detect falls, which is not suitable for outdoor travel, and multithreaded/3D LiDAR is more expensive than cameras and other sensors.…”

mentioning

confidence: 99%

Fall Detection System on Smart Walker Based on Multisensor Data Fusion and SPRT Method

et al. 2022

View full text Add to dashboard Cite

Traditional walkers are commonly used for the elderly in social life, which solves the basic problem of walking, but it is difficult to ensure safety when a fall occurs, and the human-computer interaction is poor. The image recognition method or the IMU sensor method fixed on the user, such as a wearable watch, is used by most of the current fall detection methods. Wearable sensors require the user's wearing operation, which is a little troublesome, and the detection accuracy is related to the way of wearing. The image recognition method requires a high-priced camera and a fixed installation position, which is unable to adapt to outdoor activities. We investigated a low-cost method of mounting the sensor on the body of the walker. We propose in this paper an improved fall detection method, namely Precondition and Limit Threshold SPRT (PLT-SPRT), and a novel fall detection system on the smart walker based on PLT-SPRT. The signals of the upper and lower limb sensors are fused based on the Kalman filter algorithm, and the admittance control parameters are obtained through the system identification method. In this study, the improved sequential probability ratio test algorithm is used to set the null hypothesis and the alternative hypothesis, construct the likelihood ratio and optimize the decision function, which is used to judge whether falls occur. The system is simulated by Matlab software, the user intention after fusion is more accurate, and the optimized decision function is judged accurately. Verified by the embedded system based on STM32 of the smart walker equipment in the real world, it can accurately identify the fallen state, with low detection delay, and the fallen state is detected about 160ms earlier than the traditional threshold-based detection algorithm, at the same time, the accuracy is higher than 94.9%, which meets the high real-time requirements of fall detection and is the ideal solution for smart walkers.

show abstract

Fall Detection System for Elderly based on 2D LiDAR: A Preliminary Study of Fall Incident and Activities of Daily Living (ADL) Detection

Cited by 12 publications

References 22 publications

A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

Towards Improvement of LSTM and SVM Approach for Multiclass Fall Detection System

Fall Detection System on Smart Walker Based on Multisensor Data Fusion and SPRT Method

Contact Info

Product

Resources

About