Fall Detection Sensor Research Articles

Ultra low-power, wearable, accelerated shallow-learning fall detection for elderly at-risk persons

This work focuses on the development and manufacturing of a wireless, wearable, low-power fall detection sensor (FDS) designed to predict and detect falls in elderly at-risk individuals. Unintentional falls are a significant risk in this demographic, often resulting from diminished physical capabilities such as reduced hand grip strength and complications from conditions like arthritis, vertigo, and neuromuscular issues. To address this, we utilize advanced low-power field-programmable gate arrays (FPGAs) to implement a fixed-function neural network capable of categorizing activities of daily life (ADLs), including the detection of falls. This system employs a Convolutional Neural Network (CNN) model, trained and validated using the Caffe deep learning framework with data collected from human subjects experiments. This system integrates an ST Microelectronics LSM6DSOX inertial measurement unit (IMU) sensor, embedded with an ultra-low-power Lattice iCE40UP FPGA, which samples and stores joint acceleration and orientation rate. Additionally, we have acquired and published a dataset of 3D accelerometer and gyroscope measurements from predefined ADLs and falls, using volunteer human subjects. This innovative approach aims to enhance the safety and well-being of older adults by providing timely and accurate fall detection and prediction.In this paper, we present an innovative approach to utilizing a compact Convolutional Neural Network (CNN) core for accelerating convolutional operations on a machine learning model, suitable for deployment on an ultra-low power FPGA.

A review: radar-based fall detection sensor

A review: radar-based fall detection sensor

Innovative Blind Stick for The Visually Impaired

Visual impairment is an appellation that alludes to the state of being visionless. Individuals with optical blockage are likely to have limitations in their independence and movement, as well as a higher risk of falling, fractures, injuries, poor mental health, and social isolation. Thus, we propose the design of a blind stick to help blind people and assist them in familiarizing themselves with a clear path. In our proposed prototype, multiple sensors are attached to the individual's stick. The sensor detects an obstruction, the receiver's output activates, and the microcontroller detects this change since the receiver's outputs are inputs to the microcontroller. This stick detects the impediment in front and reacts by vibrating or mandating the user. Furthermore, a smoke sensor, fall detection sensor, heart rate, SPO2 level sensor, and Temperature sensor are utilized to detect multiple parameters and not only indicate the individual using the blind stick but to the caregiver as well through an application connected to the blind stick. Moreover, GPS tracking is also present which helps the individual with the stick and also informs the caregiver about the location of the blind person. The results of our proposed prototype demonstrate that this electronic device is a sufficient solution for overcoming the difficulties faced by visually impaired people and their caregivers and allowing them to live their lives independently. In conclusion, our innovative blind stick offers a transformative solution for visually impaired individuals, enhancing their mobility and independence. We invite readers to delve into the full paper to discover the full extent of its capabilities and its potential to make a meaningful difference in the lives of millions worldwide.

Spacetimematter of aging – The material temporalities of later life

Material gerontology poses the question of how aging processes are co-constituted in relation to different forms of (human and non-human) materiality. This paper makes a novel contribution by asking when aging processes are co-constituted and how these temporalities of aging are entangled with different forms of materiality. In this paper, we explore the entanglements of temporality and materiality in shaping later life by framing them as spacetimematters (Barad, 2013). By drawing on empirical examples from data from a qualitative case study in a long-term care (LTC) facility, we ask how the entanglement of materiality and temporality of a fall-detection sensor co-constitutes aging. We focus on two types of material temporality that came to matter in age-boundary-making practices at this site: the material temporality of a technology-in-training and the material temporality of (false) alarms. Both are interwoven, produced and reproduced through spacetimematterings that established age-boundaries. Against the backdrop of these findings, we propose to understand age(ing) as a situated, distributed, more-than-human process of practices: It emerges in an assemblage of technological innovation discourses, problematizations of demographic change, digitized and analog practices of care and caring, bodily functioning, daily routines, institutionalized spaces and much more. Finally, we discuss the role power plays in those spacetimematterings of aging and conclude with a research outlook for material gerontology.

Data Augmentation to Address Various Rotation Errors of Wearable Sensors for Robust Pre-impact Fall Detection.

This paper proposes a novel application of data augmentation to address various rotation errors of wearable sensors for robust pre-impact fall detection. In such systems, sensor rotation errors are inevitable because of loose attachment and body movement during long deployment. Two augmented models with uniform and normal strategies were compared with a non-augmented model on the original dataset (no rotation error) and a validation dataset (with rotation error). The validation dataset was constructed with three types of rotation errors, namely, pitch, roll, and compound roll and pitch (CRP) at three levels of range (low: 15°, medium: 30°, and high: 45°). Five-fold cross validation showed the two augmented models maintained accuracy (>98.5%) as high as the non-augmented model on the original dataset but showed considerable improvements of 6.11% and 6.50% on the validation dataset, respectively. CRP error negatively affected the model accuracy the most, followed by pitch and then roll errors. In addition, the normal model had advantages over the uniform model in the low-to-medium range of error, which is expected to be the typical error range in practical applications. As for lead time, similarly, the augmented models achieved performance similar to the non-augmented model on the original dataset but showed significant improvements on the validation dataset. Data augmentation had notable capacities to address sensor rotation errors for practical applications and augmented models especially the normal model showed good potential to be embedded in a wearable system for robust pre-impact fall detection and injury prevention.

XAI-Fall: Explainable AI for Fall Detection on Wearable Devices Using Sequence Models and XAI Techniques

A fall detection system is vital for the safety of older people, as it contacts emergency services when it detects a person has fallen. There have been various approaches to detect falls, such as using a single tri-axial accelerometer to detect falls or fixing sensors on the walls of a room to detect falls in a particular area. These approaches have two major drawbacks: either (i) they use a single sensor, which is insufficient to detect falls, or (ii) they are attached to a wall that does not detect a person falling outside its region. Hence, to provide a robust method for detecting falls, the proposed approach uses three different sensors for fall detection, which are placed at five different locations on the subject’s body to gather the data used for training purposes. The UMAFall dataset is used to attain sensor readings to train the models for fall detection. Five models are trained corresponding to the five sensors models, and a majority voting classifier is used to determine the output. Accuracy of 93.5%, 93.5%, 97.2%, 94.6%, and 93.1% is achieved on each of the five sensors models, and 92.54% is the overall accuracy achieved by the majority voting classifier. The XAI technique called LIME is incorporated into the system in order to explain the model’s outputs and improve the model’s interpretability.

Innovative Shoe-Integrated System Based on Time-of-Flight Range Sensors for Fall Detection on Various Terrains

This letter presents a shoe-integrated fall detection system that is based on seven time-of-flight (ToF) range sensors to identify falls on different terrains. The ToF range sensors, which measure the real-time distance between the sensors and the ground surface, can be used to detect simultaneously the variation of the gait cycle and the geometry of the terrain. Using the statistical features of the ToF range signals and an error-correcting output codes-based model, 17 experimental activities, executed by nine participants, are classified into three classes—activities of daily life, near falls, and falls. The experimental F1-scores for these three classes are 100, 98.17, and 99.07%, respectively. The arrangement of ToF range sensors is simplified to develop a compact shoe-integrated system architecture for fall detection.

Elderly Fall Detection Devices Using Multiple AIoT Biomedical Sensors

Abstract Due to the influence of degeneration and chronic diseases of elderly people, a higher chance of fall-related injuries occurs among them. Falling is one of the accidents frequently confronted by elderly people, so this issue is worthy of concern. We propose diverse models to analyze falls through a wearable device. Then, we use Artificial Intelligence of Things (AIoT) biomedical sensors for fall detection to build a system for monitoring elderly people’s falls caused by dementia. The system can meet the safety needs of elderly people by providing communication, position tracking, fall detection, and pre-warning services. This device can be worn on the waist of an elderly people. Moreover, the device can monitor whether or not the person is walking normally, transmit the information to the rear-end system, and inform his/her family member via a cellphone app while an accident is occurring. Considering the risks on the fall test of elderly people, this study adopts activities of daily living (ADL) to verify the test. According to the test results, the accuracy of fall detection is 93.7%, the false positive rate is 6.2%, and the false negative rate is 6.5%. To improve the accuracy of fall detection and the timely handling of appropriate referrals, may be highly expected to reduce the occurrence of fall-related injuries. JEL classification numbers: D61, I30, O32. Keywords: Fall Detection, AIoT Sensor, Elderly People.

FPGA-based Edge Inferencing for Fall Detection.

In the geriatric population, physical injuries sustained by an unintentional or an unpredictable fall on a hard surface is the leading cause of injury related morbidity and sometimes mortality. Each year, close to 30% of adults around the age group of 65 fall down at least once. In the year 2015, close to 2.9 million falls were reported, resulting in 33,000 deaths. As much as 61% of elderly nursing home residents fell at some point during their first year of residence.These falls may aggravate the situation leading to bone fracture, concussion, internal bleeding or traumatic brain injury when immediate medical attention is not offered to the person. Delay in course of the event may sometimes lead to death as well. Recently, many studies have come up with wearable devices. These devices that are now commercially available in the market are small, compact, wireless, battery operated and power efficient. This study discusses the findings that the optimal location for a Fall Detection Sensor on the human body is in front of the Shin bone. This is based on the 183 features collected from Inertial Measurement Unit (IMU) sensors placed on 16 human body locations and trained-tested using Convolutional Neural Networks (CNN) machine learning paradigm. The ultimate goal is to develop a mobile, wireless, wearable, low-power medical device that uses a small Lattice iCE40 Field Programmable Gate Array (FPGA) integrated with gyro and accelerometer sensors which detects whether the device wearer has fallen or not. This FPGA is capable of realizing the Neural Network model implemented in it. This Insitu or Edge inferencing wearable device is capable of providing real-time classifications without any Transmitting or Receiving capabilities over a wireless communication channel.

Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics.

Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.

The Novel Computer Aided Falling Detection System Using Mobile Phone for Elderly Person

Falls are dangerous for the elderly population; therefore, many fall detection systems have been developed. However, previous methods are bulky for elderly people or only use a single sensor to isolate falls from daily living activities, which makes a fall difficult to distinguish. In this paper, we present a cost-effective and easy-to-use portable fall-detection sensor and algorithm. Specifically, to detect human falls, we used a three-axis accelerator and a three-axis gyroscope in a mobile phone. We used the Fourier descriptor-based frequency analysis method to classify both normal and falling status. From the experimental results, the proposed method detects falling status with 96.14% accuracy.

A Skeleton-Free Fall Detection System From Depth Images Using Random Decision Forest

Interest in enhancing medical services and healthcare is emerging exploiting recent technological capabilities. An integrable fall detection sensor is an essential component toward achieving smart healthcare solutions. Traditional vision-based methods rely on tracking a skeleton and estimating the change in height of key body parts such as head, hips, and shoulders. These methods are often challenged by occluded body parts and abrupt posture changes. This paper presents a fall detection system consisting of a novel skeleton-free posture recognition method and an activity recognition stage. The posture recognition method analyzes local variations in depth pixels to identify the adopted posture. An input depth frame acquired using a Kinect-like sensor is densely represented using a depth comparison feature and fed to a random decision forest to discriminate among standing, sitting, and fallen postures. The proposed approach simplifies the posture recognition into a simple pixel labeling problem, after which determining the posture is as simple as counting votes from all labeled pixels. The falling event is recognized using a support vector machine. The proposed approach records a sensitivity rate of 99% on synthetic and live datasets as well as a specificity rate of 99% on synthetic datasets and 96% on popular live datasets without invasive accelerometer support.

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning.

This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA).

Running functional sport vest and short for e-textile applications

Sports garments with functional properties have become crucial as well as comfort properties since they improve the wearer performance. For this reason, sport vest and short having high elastic recovery with fall detection sensor, were designed and produced by using flat-bed knitting machine. Comfort properties of short and vest were tested with several test instruments and; tensile strength of elastomeric yarn, air permeability, moisture management, drape and objective handle (FAST tests) of garments were achieved. It was proved that short and vest samples have good comfort properties as a functional sport garment. It was also tested that fall-detection sensor can work efficiently by using a mobile phone application.

Evaluation of Sensor Technology to Detect Fall Risk and Prevent Falls in Acute Care

Sensor technology that dynamically identifies hospitalized patients' fall risk and detects and alerts nurses of high-risk patients' early exits out of bed has potential for reducing fall rates and preventing patient harm. During Phase 1 (August 2014-January 2015) of a previously reported performance improvement project, an innovative depth sensor was evaluated on two inpatient medical units to study fall characteristics. In Phase 2 (April 2015-January 2016), a combined depth and bed sensor system designed to assign patient fall probability, detect patient bed exits, and subsequently prevent falls was evaluated. Fall detection depth sensors remained in place on two medicine units; bed sensors used to detect patient bed exits were added on only one of the medicine units. Fall rates and fall with injury rates were evaluated on both units. During Phase 2, the designated evaluation unit had 14 falls, for a fall rate of 2.22 per 1,000 patient-days-a 54.1% reduction compared with the Phase 1 fall rate. The difference in rates from Phase 1 to Phase 2 was statistically significant (z = 2.20; p = 0.0297). The comparison medicine unit had 30 falls-a fall rate of 4.69 per 1,000 patient-days, representing a 57.9% increase as compared with Phase 1. A fall detection sensor system affords a level of surveillance that standard fall alert systems do not have. Fall prevention remains a complex issue, but sensor technology is a viable fall prevention option.

Radar and RGB-Depth Sensors for Fall Detection: A Review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.

Smart Phone Based Fall Detection using Auto Regression Modeling in a Non-Restrictive Setting

Fall detection is an important aspect of the field of accident prevention, ambient assisted living as well as care of the elderly. To address this issue, researchers have employed several approaches including vision based systems, setups that require deployment in a special environment and inertial sensors. Inertial sensors have the advantage of being deployable in mobile systems such as wearable devices and smart phones. An important consideration in using inertial sensors for fall detection is the need to develop techniques that would work without enforcing positional requirements of the sensor device. This paper presents a method for the detection of falls using inertial sensors readings of the smart phone, a tri-axial accelerometer, tri-axial gyroscope and orientation data. We consider inertial sensor data for two falls and three activities of daily living. Using Auto-Regressive (AR) modeling to characterize the measurements from the sensors, we compare Support Vector Machines (SVM) and Neural Networks for use in classifying between these five events. Results indicate that the Neural Network provides better classification accuracy compared to SVM for the purpose of differentiating between falls and the activities of daily living.

Testing non-wearable fall detection methods in the homes of older adults.

In this paper, we describe two longitudinal studies in which fall detection sensor technology was tested in the homes of older adults. The first study tested Doppler radar, a two-webcam system, and a depth camera system in ten apartments for two years. This continuous data collection allowed us to investigate the real-world setting of target users and compare the advantages and limitations of each sensor modality. Based on this study, the depth camera was chosen for a current ongoing study in which depth camera systems have been installed in 94 additional older adult apartments. We include a discussion of the different sensor systems, the pros and cons of each, and results of the fall detection and false alarms in the older adult homes.

Anatomy of Inpatient Falls: Examining Fall Events Captured by Depth-Sensor Technology

Sensor technology offers a new way to identify patient movement, detect falls, and automatically alert health care staff when falls occur. The information gained from analyzing actual fall events can be beneficial in developing individualized fall prevention strategies, informing nursing staff about the nature of falls, and identifying opportunities to make the patient care environment safer. A six-month performance improvement pilot was conducted at Barnes-Jewish Hospital (St. Louis) to assess the ability of a depth-sensor system to capture inpatient fall events within patient hospital rooms. Depth sensors were installed on two inpatient medicine units with a history of high fall rates. The depth sensors captured actual fall events on video. Video clips were reviewed and analyzed to identify the characteristics of patient falls, staff response times, and environmental conditions contributing to falls. A total of 16 falls involving 13 patients were recorded by depth sensors. Six of the 13 patients who fell were classified as high risk on the basis of the hospital's fall rating tool. Common contributing factors included difficulty rising from their bed, weakened lower extremities, and unsteady or slow gait. Eleven of the falls involved patients reaching for objects in their path in an effort to achieve stability. Nurses had less than two minutes from the time a patient began to exit a bed to the time a fall occurred. Patients expressed few complaints with depth sensors installed in rooms. Fall-detection sensor systems offer valuable data for analyzing the nature of patient falls, with the potential promise of prescribing specific fall interventions for patients and to identify staff development opportunities. Hospitals should understand these devices' benefits and limitations and how they affect nursing practice.

Mobile Phone Based Falling Detection Sensor and Computer-Aided Algorithm for Elderly People

Falls are dangerous for the elderly population; therefore many fall detection systems have been developed. However, previous methods are bulky for elderly people or only use a single sensor to isolate falls from daily living activities, which makes a fall difficult to distinguish. In this paper, we present a cost-effective and easy-to-use portable fall-detection sensor and algorithm. Specifically, to detect human falls, we used a three-axis accelerator and a three-axis gyroscope in a mobile phone. We used the Fourier descriptor-based frequency analysis method to classify both normal and falling status. From the experimental results, the proposed method detects falling status with 96.14% accuracy.