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Huang, M.; Garcia, A. Fall Detection and Activity Logging Using Motion Classification. Encyclopedia. Available online: https://encyclopedia.pub/entry/50835 (accessed on 07 July 2024).
Huang M, Garcia A. Fall Detection and Activity Logging Using Motion Classification. Encyclopedia. Available at: https://encyclopedia.pub/entry/50835. Accessed July 07, 2024.
Huang, Maxwell, Antony Garcia. "Fall Detection and Activity Logging Using Motion Classification" Encyclopedia, https://encyclopedia.pub/entry/50835 (accessed July 07, 2024).
Huang, M., & Garcia, A. (2023, October 26). Fall Detection and Activity Logging Using Motion Classification. In Encyclopedia. https://encyclopedia.pub/entry/50835
Huang, Maxwell and Antony Garcia. "Fall Detection and Activity Logging Using Motion Classification." Encyclopedia. Web. 26 October, 2023.
Fall Detection and Activity Logging Using Motion Classification
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This entry is adapted from the peer-reviewed paper 10.3390/app132011379

The entry presents a novel approach to detect falls in people who use orthopedic walkers, especially in older people and people with limited mobility. Recognizing that walker users may not wear traditional wearable devices, such as bracelets, consistently, the researchers propose connecting an Internet of Things (IoT) device equipped with an inertial measurement unit (IMU) sensor directly to the walker. This setup aims to provide real-time fall detection and activity logging. To support this system, a data set capturing four distinct activities of walker users was collected and labeled: idle, motion, step, and fall.

orthopedic walker dataset IoT fall detection activity logging

1. Introduction

According to a report from the World Health Organization (WHO), the percentage of the world’s population over 60 years old is projected to nearly double from 12% to 22% between 2015 and 2050, increasing from 1 billion in 2020 to 1.4 billion by 2030 and nearly 2.1 billion by 2050 [1]. One of the common problems in the elderly population is accidental falls, which are common but sometimes life-threatening. The rising demand for fall detection systems, algorithms, and techniques has been evident in the surge of interest observed via Google Trends, a platform dedicated to monitoring internet users’ information search patterns since 2004. Notably, the search term “fall detection” has attained an unprecedented peak, registering a remarkable surge of over 500% within the past five years [2].
There are approximately 6.1 million people in the United States who use mobility assistance devices, including walking canes, orthopedic walkers, and rollators. However, life-threatening falls in the older population are a crucial health and safety issue; about 1.5 million elderly people are injured by falling each year, and about 47,300 people per year using walking aids suffer injuries from falls that require an emergency room visit [3].
In addition, life-threatening falls often occur within the rehabilitation process after major surgery to the hip or leg area, which is an increasing concern for patients and medical experts alike. The risk of repeat falls is especially high in patients who have already sustained a hip fracture [4]. Thus, there is an urgent need for a smart device that can detect falls for orthopedic walker users in real time and alert caregivers for emergency assistance.
In recent years, wearable smart devices, such as Fitbit and Apple Watch, have become very popular for activity tracking and physiology monitoring; however, these smartwatches are not designed for walker users. Even if a walker user wears such a device on the wrist, the device may not accurately detect motions and hazards since both hands are rested on the walker during movement. In addition, some elderly users may not wear such devices reliably on their own. Traditional at-home health monitoring products often abandon the usage of tracking devices due to irregularities and false positives in favor of simple medical alert systems self-actuated by the subject, which do not consider the possibility that a fall may cause life-threatening injuries that may incapacitate the subject and cause them to be unable to activate such systems. Inhibitions to speech and capability of motion can render medical alert systems that rely on direct communication between emergency services and the user ineffective.

2. Dataset and System Design for Orthopedic Walker Fall Detection and Activity Logging Using Motion Classification

A wide range of approaches have been explored to design effective fall detection systems, including the use of wearable devices [5][6][7][8][9] and smartphones [10][11][12][13] with microphones [14], cameras [15], accelerometers and gyroscopes [16][17], GPS [18], and combinations of multiple sensors [19].
In addition to wearable devices, alternative approaches have been extensively investigated in fall detection systems. Depth sensors, such as Microsoft Kinect [20] or time-of-flight cameras [21], have been implemented with the primary goal of enhancing the accuracy and effectiveness of fall detection systems. Infrared sensors have been used to detect changes in infrared radiation within specific environments [22]. Doppler radar systems provide a non-invasive and privacy-preserving approach for timely and accurate fall detection among the elderly, analyzing unique time-frequency characteristics to identify fall events regardless of lighting conditions [23].
Vision-based methods analyze video data from cameras, employing techniques such as optical flow analysis [24], object tracking [25], and human pose estimation to detect falls based on changes in motion or posture [26]. Acoustic sensors, such as microphones or sound arrays, capture audio signals and employ signal processing techniques to identify sudden impact sounds, screams, or other acoustic patterns associated with falls [27].
Signal processing techniques play a crucial role in all of the aforementioned fall detection systems, as they extract meaningful features from sensor data, facilitating the accurate detection of fall events across diverse system types. Multiple signal processing techniques have been employed in fall detection systems, including orientation filters [7], quaternions [8], thresholding techniques [11][16][17][18], histograms of oriented gradients [15], clustering algorithms [19], Bayesian segmentation approaches [21], spatiotemporal analysis methods [25], Kalman Filters [28], sensor data fusion [29], and wavelet transforms [30].
Furthermore, machine learning methods have been applied extensively in recent years. In this field, a variety of machine learning models have been employed, ranging from simpler approaches, like decision trees [20] and k-nearest neighbors (kNN) [5][9][12][19][22][27], to more complex methods, such as Bayesian classifiers [21][23], support vector machines (SVM) [26], neural networks [14], and deep learning models [13][24].
A closely related research field is human activity recognition (HAR) by classifying human activities from motion sensor data. Several public datasets for fall detection using wearable sensors [31] are available. A dataset [32] in which data were labeled as fall, near fall, and activities of daily living (ADL) were most useful for us. Both the signal processing method [33] and the machine learning approach [34][35] are widely used in this field. In signal processing approaches, methods based on preset thresholds to detect a step [36], methods based on peak detection count steps by counting the peaks of sensor readings [37], and methods based on correlation analysis count steps by calculating and comparing the correlation coefficients between two neighboring windows of sensor readings [38]. In machine learning methods, authors design HAR algorithms based on convolutional neural networks (CNN) [39][40], and researchers in [35] have developed an algorithm based on long short-term memory (LSTM) to recognize human activities. It is worth mentioning that all existing HAR datasets were based on wearable IMU sensors directly attached to the human body. In contrast, this project involves fixing the sensor to a walker, so the data characteristics are quite different.
The research and development of fall detection systems specifically for assistive walkers and rollators is relatively limited. However, there have been several notable endeavors to create technology-integrated devices to improve the safety and functionality of these mobility aids.
In [41], a rollator-based ambulatory assistive device with an integrated non-obtrusive monitoring system was introduced, aiming to enhance the functionality and capabilities of traditional rollators. The Smart Rollator prototype incorporates multiple subsystems, including distance/speed measurement, acceleration analysis, force sensing, seat usage tracking, and physiological monitoring. Collected data are stored locally within a microprocessor system and periodically transferred to a remote data server via a local data terminal. This enables remote access and analysis of the data, contributing to improved monitoring and support for individuals using rollators.
The research work in [42] presents a fall detection system designed specifically for smart walkers. The system combines signal processing techniques with the probability likelihood ratio test and sequential probability ratio test (PLT-SPRT) algorithm to achieve accurate fall detection. Simulation experiments were conducted to identify the control model of the walker and analyze limb movements, while real-world experiments were performed to validate the system’s performance.
The study in [43] demonstrates the experimental results of fall detection and prevention using a cane robot. Non-disabled male participants walked with the cane robot, and their “normal walking” and “abnormal walking” data were recorded. The fall detection rate was evaluated using the center of pressure-based fall detection (COP-FD) and leg-motion-based fall detection (LM-FD) methods. COP-FD detected falls by calculating the center of pressure (COP) during walking and comparing it to predefined thresholds. LM-FD used a laser range finder (LRF) to measure the relative distance between the robot and the user’s legs, enabling the detection of leg motion abnormalities associated with stumbling. The results showed successful fall detection for both COP-FD and LM-FD, with some instances of false positives and false negatives.
While various machine learning techniques using sensor data have been explored for fall detection systems, a major obstacle in these projects is the lack of reliable data. Obtaining large and diverse datasets that capture different fall scenarios and environmental conditions is crucial for training robust models, posing a significant challenge that researchers and developers must address for effective machine learning-based fall detection systems.
The study in [31] presents a comprehensive analysis of publicly available datasets used for research on wearable fall detection systems. The study examines twelve datasets and compares them based on various factors, including experimental setup, sensor characteristics, movement emulation, and data format. The datasets primarily use accelerometers, gyroscopes, and magnetometers as the main sensors for capturing movement and orientation data. However, there is a lack of consensus and standardization among the datasets regarding the number and position of sensors, as well as the specific models and characteristics of the sensors employed. This heterogeneity makes it challenging to compare and evaluate fall detection systems effectively.

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Subjects: Engineering, Electrical & Electronic
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