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

Comprehensive review of vision-based fall detection systems

Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection

Real-Time Fall Detection and Response on Mobile Phones Using Machine Learning

Falls are common and often dangerous for groups with impaired mobility, like the elderly or people with lower limb amputations. Finding ways of minimizing the frequency or impact of a fall can improve quality of life dramatically. When someone does fall, real-time detection of the fall and a long-lie can trigger fast medical assistance. Such a system can also collect reliable data on the nature of real-world falls that can be used to better understand the circumstances, to aid in prevention efforts. This work has been to develop a real-time fall tracking system specifically for subjects with lower limb amputations. In this study 17 subjects (10 healthy controls and 7 amputees) were asked to simulate 4 types of falls (trip, slip, right and left lateral) 3 times each with a mobile phone placed at 3 different locations on the body (pouch, pocket, and hand). Signals were collected from the accelerometer, gyroscope and barometer sensors using the Android mobile phone application Purple Robot. We compared 5 different machine learning classifiers for fall detection: logistic regression (L1 and L2 norm), support vector machines, K-nearest neighbors, decision trees, and random forest. Logistic regression (L1 regularized lasso ) and random forest yielded the best results on the test set (98.8% and 98.4%, respectively). There was no significant difference between amputee and healthy control falls in terms of classifier accuracy. When testing on real world data with no recorded falls, the false positive rate was only 0.07%. In addition to offline algorithmic development, the detection system was implemented for real-time application on a mobile platform. The previously-trained logistic regression model was implemented on the mobile platform for real-time detection. This platform will be used in an upcoming amputee population falls study. The completed system will gather data on the current conditions leading to the fall (weather, GPS location, etc.) and classify the type of the fall. The system will follow up with notifications requesting a response from the user, or automatically notify emergency contacts or 911 as needed. The steps taken in creating this system bring us closer to real-time intervention strategies to minimize the impact of falls, and enable us to collect accurate falls-related data to improve fall prevention strategies and prosthesis design

A Machine Learning Classification Paradigm for Automated Human Fall Detection

For elderly people, falls are a severe worry since they can result in serious injuries, loss of independence, and deterioration of general health. In fact, among older persons, falls constitute the main reason for injury-related hospitalisations and fatalities. There is an obvious demand for fall detection systems that can help avoid or lessen the negative effects of falls given the enormous impact of falls on the senior population. Systems for detecting falls are created to notify carers or emergency services when a person has fallen, enabling quicker responses and better results. Elderly people who live alone or have mobility or balance impairments that make them more likely to fall may find these systems to be especially helpful. The difficulty of categorising various actions as part of a system created to meet the demand for a wearable device to collect data for fall and near-fall analysis is addressed in this study. Three common activities—standing, walking, and lying down—four distinct fall trajectories—forward, backward, left, and right—as well as near-fall circumstances are recognised and detected. Overall, fall detection systems play a significant role in the care of elderly people by lowering the chance of falls and its unfavourable effects. In order to better meet the demands of this vulnerable group, it's expected that as the older population grows, there will be a greater demand for fall detection systems and ongoing technological developments

Radar for Assisted Living in the Context of Internet of Things for Health and Beyond

This paper discusses the place of radar for assisted living in the context of IoT for Health and beyond. First, the context of assisted living and the urgency to address the problem is described. The second part gives a literature review of existing sensing modalities for assisted living and explains why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that helps improve performances in classification especially with deep learning with a reflection on lessons learned from it. The fourth section introduces recent published work from our research group in the area that shows promise with multimodal sensor fusion for classification and long short-term memory applied to early stages in the radar signal processing chain. Finally, we conclude with open challenges still to be addressed in the area and open to future research directions in animal welfare

Sistema de Deteção de Quedas Automático Baseado em Vídeo

The elderly population faces difficulties in completing certain tasks independently, often re quiring supervision to not only assist them but also to mitigate and notify about potential health risks. Falls, a prevalent and severe problem, pose a high risk of causing hospitaliza tions and fatalities. However, the aging population in developed countries is growing at an unprecedented rate, while the proportion of active age individuals continues to decline. Con sequently, elderly care has become less accessible as caregivers are confronted with a larger number of patients. Nonetheless, conventional fall detection methods, typically triggered by victims themselves, are unreliable and inadequate. This thesis proposes an automatic alternative to existing methods, presenting a computer vision-based Fall Detection System (FDS) that utilizes a two-stream Inflated 3D Convolutional Neural Network (I3D) in con junction with a Recurrent Neural Network (RNN). To enhance the available datasets, a new collection of simulated falls was created. Experimental evaluations demonstrate the superi ority of this hybrid model over state-of-the-art fall detection models, achieving an accuracy of 94% and a recall value of 96%. By promptly and accurately detecting falls, a system employing this model could significantly reduce the risk of severe injuries posed to the elderly and physically disabled individuals.Os idosos enfrentam dificuldades em completar certas tarefas sozinhos e precisam de su pervisão frequente, não só para assistí-los, mas também para mitigar e alertar para riscos potenciais de saúde. Quedas são problemas prevalentes e sérios, muitas vezes resultando em hospitalizações ou mortes. Contudo, nos países desenvolvidos, a população idosa está a crescer e a proporção de cidadãos de idade ativa a diminuir. Por consequência, cuidados a idosos tornam-se mais inacessíveis, já que enfermeiros são confrontados com um maior número de pacientes. Não obstante, métodos convencionais de deteção de quedas, que requerem, normalmente, a ativação por parte da vítima, não são confiáveis nem adequados. Esta tese propõe uma alternativa automática a estes métodos na forma de um sistema de deteção de quedas que incorpora uma rede neuronal convolucional 3D juntamente com uma rede neuronal recorrente. Para melhorar os datasets já existentes, uma nova coleção de vídeos de quedas foi criada. Este modelo híbrido revela ter performances superiores às de outros modelos, conseguindo uma acurácia de 94% e uma sensitividade de 96%. Ao ser capaz de detetar quedas precisa e imediatamente, um sistema que inclui este modelo poderá reduzir drasticamente o risco de ferimentos graves aos idosos e pessoas com deficiências físicas

Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms

openCon una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia è in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attività di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.INGEGNERIA INDUSTRIALEopenPirozzi, Michel

Early detection of health changes in the elderly using in-home multi-sensor data streams

The rapid aging of the population worldwide requires increased attention from health care providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen, so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this work, we investigate a methodology for learning data distribution from streaming data and tracking the evolution of the behavior trajectories over long periods (years) using high dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a change in behavior, albeit not highly specific, tracking trajectory deviations can provide hints of early illness. Retrospectively, we visualize the streaming clustering results and track how the behavior clusters evolve in feature space with the help of two dimension-reduction algorithms, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Moreover, our tracking algorithm in the original high dimensional feature space generates early health warning alerts if a negative trend is detected in the behavior trajectory. We validated our algorithm on synthetic data, real-world data and tested it on a pilot dataset of four TigerPlace residents monitored with a collection of motion, bed, and depth sensors over ten years. We used the TigerPlace electronic health records (EHR) to understand the residents' behavior patterns and to evaluate and explain the health warnings generated by our algorithm. The results obtained on the TigerPlace dataset show that most of the warnings produced by our algorithm can be linked to health events documented in the EHR, providing strong support for a prospective deployment of the approach.Includes bibliographical references