Deep Learning-Based Near-Fall Detection Algorithm for Fall Risk Monitoring System Using a Single Inertial Measurement Unit

Choi, Ahnryul; Kim, Tae Hyong; Yuhai, Oleksandr; Jeong, Soo‐Hwan; Kim, Kyung-Ran; Kim, Hyunggun; Mun, Joung Hwan

doi:10.1109/tnsre.2022.3199068

Cited by 41 publications

(16 citation statements)

References 49 publications

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“…Many fall prediction algorithms exist, and there are multiple open source datasets available [11] [12] [13] [14] [15] [16]. However, there are few data trials in which a mobility aid was used.…”

Section: B Prior Work In Fall Prediction and Injury Preventionmentioning

confidence: 99%

“…Both false negative and false positive rates must be low. In the literature obtaining low false positive rates below 1%, or sensitivity scores above 99%, has been hard to achieve [15] [11]. Impractically high false positive rates are a major barrier when used in conjunction with a physical means to prevent a fall or reduce risk of injury.…”

Section: Prediction Of Time Remaining Until Start Of the Descent Phasementioning

confidence: 99%

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Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries

Kamienski,

Bonato,

Asada

2024

IEEE Access

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Falls may cause serious injuries to older adults, leading to a deteriorated quality of life. Currently, fall injury prevention devices are lacking, especially for the balance impaired population who rely on mobility aids. Here, the functionality of a walker is augmented, so that it can predict a fall in real-time and prevent fall injuries with a rapidly reconfigurable mechanism. A key challenge is real-time fall prediction, which is a time-critical decision making process. A fall must be predicted preemptively so that the system has sufficient time to deploy the injury prevention mechanism. Data are collected from human subjects undergoing diverse loss-of-balance situations while using a walker. A predictor based on multiple Long-Short Term Memory (LSTM) networks is constructed based on two novel techniques. One is to construct a "Timer LSTM" that estimates the time remaining before the fall prevention mechanism must be activated, so that if time allows, additional data are collected and the possibility of a fall is further examined. This lowered the fall prediction false positive rate. Second, confounding cases are further analyzed using a metric of data deficiency, called the Lipschitz quotient. Additional data features that lower the Lipschitz quotients and, thereby, increase data predictability, are sought and incorporated into the original input signals. Augmenting the data further improved performance, and the best model had a 97% identified falls rate at a 0.17% false positive rate. The prediction method is implemented on a novel walker-type fall prediction and prevention prototype. The walker has a small footprint for improved maneuverability, and becomes untippable when its expandable legs are deployed in the event of a predicted fall. Thus, the older adult tethered to the untippable walker is protected from a fall. This promises immense benefits for future research on improving older adult wellbeing through real-time fall protection.

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Section: B Prior Work In Fall Prediction and Injury Preventionmentioning

confidence: 99%

Section: Prediction Of Time Remaining Until Start Of the Descent Phasementioning

confidence: 99%

Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries

Kamienski,

Bonato,

Asada

2024

IEEE Access

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“…Due to their sophisticated implementations and high cost, they are not always viable. On the other hand, wearable devices have taken advantage of low-cost sensors embedded in general-purpose devices that people can wear to be monitored [22][23][24]. Nevertheless, the subject's will to wear such sensing devices is of prime importance because detection accuracy depends on it.…”

Section: Related Workmentioning

confidence: 99%

“…Our efforts are focused on the implementation of two DL frameworks capable of accurately detecting falling events and distinguishing them from human activities. The LSTM network and CNN have shown remarkable success in performing fall detection tasks [12,23,35].…”

Section: System Overviewmentioning

confidence: 99%

Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures

Cardenas

Gutiérrez

Aguilar-Ponce

2023

IJERPH

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Falling events are a global health concern with short- and long-term physical and psychological implications, especially for the elderly population. This work aims to monitor human activity in an indoor environment and recognize falling events without requiring users to carry a device or sensor on their bodies. A sensing platform based on the transmission of a continuous wave (CW) radio-frequency (RF) probe signal was developed using general-purpose equipment. The CW probe signal is similar to the pilot subcarriers transmitted by commercial off-the-shelf WiFi devices. As a result, our methodology can easily be integrated into a joint radio sensing and communication scheme. The sensing process is carried out by analyzing the changes in phase, amplitude, and frequency that the probe signal suffers when it is reflected or scattered by static and moving bodies. These features are commonly extracted from the channel state information (CSI) of WiFi signals. However, CSI relies on complex data acquisition and channel estimation processes. Doppler radars have also been used to monitor human activity. While effective, a radar-based fall detection system requires dedicated hardware. In this paper, we follow an alternative method to characterize falling events on the basis of the Doppler signatures imprinted on the CW probe signal by a falling person. A multi-class deep learning framework for classification was conceived to differentiate falling events from other activities that can be performed in indoor environments. Two neural network models were implemented. The first is based on a long-short-term memory network (LSTM) and the second on a convolutional neural network (CNN). A series of experiments comprising 11 subjects were conducted to collect empirical data and test the system’s performance. Falls were detected with an accuracy of 92.1% for the LSTM case, while for the CNN, an accuracy rate of 92.1% was obtained. The results demonstrate the viability of human fall detection based on a radio sensing system such as the one described in this paper.

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“…There is a rapid growth in the use of wearable sensors for fall detection systems, using inertial sensors such as accelerometers, gyroscopes, magnetometers, and inertial measurement units (IMU), as well as pressure sensors [ 3 , 4 ]. Several studies have demonstrated the effectiveness of using accelerometers on the waist and wireless accelerometers in identifying fall events with high accuracy rates ranging from 86% to 99% [ 5 , 6 ]. Considerable efforts have been devoted to creating wearable devices that can detect falls after they occur to ensure prompt medical assistance.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors

Mohammad

Anwary

Mridha

et al. 2023

Sensors

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Fatal injuries and hospitalizations caused by accidental falls are significant problems among the elderly. Detecting falls in real-time is challenging, as many falls occur in a short period. Developing an automated monitoring system that can predict falls before they happen, provide safeguards during the fall, and issue remote notifications after the fall is essential to improving the level of care for the elderly. This study proposed a concept for a wearable monitoring framework that aims to anticipate falls during their beginning and descent, activating a safety mechanism to minimize fall-related injuries and issuing a remote notification after the body impacts the ground. However, the demonstration of this concept in the study involved the offline analysis of an ensemble deep neural network architecture based on a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) and existing data. It is important to note that this study did not involve the implementation of hardware or other elements beyond the developed algorithm. The proposed approach utilized CNN for robust feature extraction from accelerometer and gyroscope data and RNN to model the temporal dynamics of the falling process. A distinct class-based ensemble architecture was developed, where each ensemble model identified a specific class. The proposed approach was evaluated on the annotated SisFall dataset and achieved a mean accuracy of 95%, 96%, and 98% for Non-Fall, Pre-Fall, and Fall detection events, respectively, outperforming state-of-the-art fall detection methods. The overall evaluation demonstrated the effectiveness of the developed deep learning architecture. This wearable monitoring system will prevent injuries and improve the quality of life of elderly individuals.

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Deep Learning-Based Near-Fall Detection Algorithm for Fall Risk Monitoring System Using a Single Inertial Measurement Unit

Cited by 41 publications

References 49 publications

Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries

Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries

Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures

An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors

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