Walking Step Monitoring with a Millimeter-Wave Radar in Real-Life Environment for Disease and Fall Prevention for the Elderly

We studied the use of a millimeter-wave frequency-modulated continuous wave radar for gait analysis in a real-life environment, with a focus on the measurement of the step time. A method was developed for the successful extraction of gait patterns for different test cases. The quantitative investigation carried out in a lab corridor showed the excellent reliability of the proposed method for the step time measurement, with an average accuracy of 96%. In addition, a comparison test between the millimeter-wave radar and a continuous-wave radar working at 2.45 GHz was performed, and the results suggest that the millimeter-wave radar is more capable of capturing instantaneous gait features, which enables the timely detection of small gait changes appearing at the early stage of cognitive disorders

Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis

The applicability of Doppler radar for gait analysis is investigated by quantitatively comparing the measured biomechanical parameters to those obtained using motion capturing and ground reaction forces. Nineteen individuals walked on a treadmill at two different speeds, where a radar system was positioned in front of or behind the subject. The right knee angle was confined by an adjustable orthosis in five different degrees. Eleven gait parameters are extracted from radar micro-Doppler signatures. Here, new methods for obtaining the velocities of individual lower limb joints are proposed. Further, a new method to extract individual leg flight times from radar data is introduced. Based on radar data, five spatiotemporal parameters related to rhythm and pace could reliably be extracted. Further, for most of the considered conditions, three kinematic parameters could accurately be measured. The radar-based stance and flight time measurements rely on the correct detection of the time instant of maximal knee velocity during the gait cycle. This time instant is reliably detected when the radar has a back view, but is underestimated when the radar is positioned in front of the subject. The results validate the applicability of Doppler radar to accurately measure a variety of medically relevant gait parameters. Radar has the potential to unobtrusively diagnose changes in gait, e.g., to design training in prevention and rehabilitation. As contact-less and privacy-preserving sensor, radar presents a viable technology to supplement existing gait analysis tools for long-term in-home examinations.Comment: 13 pages, 9 figures, 2 tables, accepted for publication in the IEEE Journal of Biomedical and Health Informatics (J-BHI

Detection of Gait Asymmetry Using Indoor Doppler Radar

Doppler radar systems enable unobtrusive and privacy-preserving long-term monitoring of human motions indoors. In particular, a person's gait can provide important information about their state of health. Utilizing micro-Doppler signatures, we show that radar is capable of detecting small differences between the step motions of the two legs, which results in asymmetric gait. Image-based and physical features are extracted from the radar return signals of several individuals, including four persons with different diagnosed gait disorders. It is shown that gait asymmetry is correctly detected with high probability, irrespective of the underlying pathology, for at least one motion direction.Comment: 6 pages, 5 figures, 4 tables; accepted at the IEEE Radar Conference 2019, Boston, MA, US

Toward Unobtrusive In-home Gait Analysis Based on Radar Micro-Doppler Signatures

Objective: In this paper, we demonstrate the applicability of radar for gait classification with application to home security, medical diagnosis, rehabilitation and assisted living. Aiming at identifying changes in gait patterns based on radar micro-Doppler signatures, this work is concerned with solving the intra motion category classification problem of gait recognition. Methods: New gait classification approaches utilizing physical features, subspace features and sum-of-harmonics modeling are presented and their performances are evaluated using experimental K-band radar data of four test subjects. Five different gait classes are considered for each person, including normal, pathological and assisted walks. Results: The proposed approaches are shown to outperform existing methods for radar-based gait recognition which utilize physical features from the cadence-velocity data representation domain as in this paper. The analyzed gait classes are correctly identified with an average accuracy of 93.8%, where a classification rate of 98.5% is achieved for a single gait class. When applied to new data of another individual a classification accuracy on the order of 80% can be expected. Conclusion: Radar micro-Doppler signatures and their Fourier transforms are well suited to capture changes in gait. Five different walking styles are recognized with high accuracy. Significance: Radar-based sensing of human gait is an emerging technology with multi-faceted applications in security and health care industries. We show that radar, as a contact-less sensing technology, can supplement existing gait diagnostic tools with respect to long-term monitoring and reproducibility of the examinations.Comment: 11 pages, 6 figure

Risk of falling in a timed Up and Go test using an UWB radar and an instrumented insole

Previously, studies reported that falls analysis is possible in the elderly, when using wearable sensors. However, these devices cannot be worn daily, as they need to be removed and recharged from time-to-time due to their energy consumption, data transfer, attachment to the body, etc. This study proposes to introduce a radar sensor, an unobtrusive technology, for risk of falling analysis and combine its performance with an instrumented insole. We evaluated our methods on datasets acquired during a Timed Up and Go (TUG) test where a stride length (SL) was computed by the insole using three approaches. Only the SL from the third approach was not statistically significant (p = 0.2083 > 0.05) compared to the one provided by the radar, revealing the importance of a sensor location on human body. While reducing the number of force sensors (FSR), the risk scores using an insole containing three FSRs and y-axis of acceleration were not significantly different (p > 0.05) compared to the combination of a single radar and two FSRs. We concluded that contactless TUG testing is feasible, and by supplementing the instrumented insole to the radar, more precise information could be available for the professionals to make accurate decision

Autonomous Radar-based Gait Monitoring System

Features related to gait are fundamental metrics of human motion [1]. Human gait has been shown to be a valuable and feasible clinical marker to determine the risk of physical and mental functional decline [2], [3]. Technologies that detect changes in people’s gait patterns, especially older adults, could support the detection, evaluation, and monitoring of parameters related to changes in mobility, cognition, and frailty. Gait assessment has the potential to be leveraged as a clinical measurement as it is not limited to a specific health care discipline and is a consistent and sensitive test [4]. A wireless technology that uses electromagnetic waves (i.e., radar) to continually measure gait parameters at home or in a hospital without a clinician’s participation has been proposed as a suitable solution [3], [5]. This approach is based on the interaction between electromagnetic waves with humans and how their bodies impact the surrounding and scattered wireless signals. Since this approach uses wireless waves, people do not need to wear or carry a device on their bodies. Additionally, an electromagnetic wave wireless sensor has no privacy issues because there is no video-based camera. This thesis presents the design and testing of a radar-based contactless system that can monitor people’s gait patterns and recognize their activities in a range of indoor environments frequently and accurately. In this thesis, the use of commercially available radars for gait monitoring is investigated, which offers opportunities to implement unobtrusive and contactless gait monitoring and activity recognition. A novel fast and easy-to-implement gait extraction algorithm that enables an individual’s spatiotemporal gait parameter extraction at each gait cycle using a single FMCW (Frequency Modulated Continuous Wave) radar is proposed. The proposed system detects changes in gait that may be the signs of changes in mobility, cognition, and frailty, particularly for older adults in individual’s homes, retirement homes and long-term care facilities retirement homes. One of the straightforward applications for gait monitoring using radars is in corridors and hallways, which are commonly available in most residential homes, retirement, and long-term care homes. However, walls in the hallway have a strong “clutter” impact, creating multipath due to the wide beam of commercially available radar antennas. The multipath reflections could result in an inaccurate gait measurement because gait extraction algorithms employ the assumption that the maximum reflected signals come from the torso of the walking person (rather than indirect reflections or multipath) [6]. To address the challenges of hallway gait monitoring, two approaches were used: (1) a novel signal processing method and (2) modifying the radar antenna using a hyperbolic lens. For the first approach, a novel algorithm based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method is proposed. This proposed algorithm could be paired with any type of multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. The algorithm functionality was validated by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a hallway. The preliminary results demonstrate the promising potential of the algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments. For the second approach, an in-package hyperbola-based lens antenna was designed that can be integrated with a radar module package empowered by the fast and easy-to-implement gait extraction method. The system functionality was successfully validated by capturing the spatiotemporal gait values of people walking in a hallway filled with metallic cabinets. The results achieved in this work pave the way to explore the use of stand-alone radar-based sensors in long hallways for day-to-day long-term monitoring of gait parameters of older adults or other populations. The possibility of the coexistence of multiple walking subjects is high, especially in long-term care facilities where other people, including older adults, might need assistance during walking. GaitRite and wearables are not able to assess multiple people’s gait at the same time using only one device [7], [8]. In this thesis, a novel radar-based algorithm is proposed that is capable of tracking multiple people or extracting walking speed of a participant with the coexistence of other people. To address the problem of tracking and monitoring multiple walking people in a cluttered environment, a novel iterative framework based on unsupervised learning and advanced signal processing was developed and tested to analyze the reflected radio signals and extract walking movements and trajectories in a hallway environment. Advanced algorithms were developed to remove multipath effects or ghosts created due to the interaction between walking subjects and stationary objects, to identify and separate reflected signals of two participants walking at a close distance, and to track multiple subjects over time. This method allows the extraction of walking speed in multiple closely-spaced subjects simultaneously, which is distinct from previous approaches where the speed of only one subject was obtained. The proposed multiple-people gait monitoring was assessed with 22 participants who participated in a bedrest (BR) study conducted at McGill University Health Centre (MUHC). The system functionality also was assessed for in-home applications. In this regard, a cloud-based system is proposed for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. Range-Doppler maps generated from a dataset of real-life in-home activities are used to train deep learning models. The performance of several deep learning models was evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices

El uso de la tecnología de captura de movimiento para el análisis del rendimiento deportivo

In sport performance, motion capture aims at tracking and recording athletes’ human motion in real time to analyze physical condition, athletic performance, technical expertise and injury mechanism, prevention and rehabilitation. The aim of this paper is to systematically review the latest developments of motion capture systems for the analysis of sport performance. To that end, selected keywords were searched on studies published in the last four years in the electronic databases ISI Web of Knowledge, Scopus, PubMed and SPORTDiscus, which resulted in 892 potential records. After duplicate removal and screening of the remaining records, 81 journal papers were retained for inclusion in this review, distributed as 53 records for optical systems, 15 records for non-optical systems and 13 records for markerless systems. Resultant records were screened to distribute them according to the following analysis categories: biomechanical motion analysis, validation of new systems and performance enhancement. Although optical systems are regarded as golden standard with accurate results, the cost of equipment and time needed to capture and postprocess data have led researchers to test other technologies. First, non-optical systems rely on attaching sensors to body parts to send their spatial information to computer wirelessly by means of different technologies, such as electromagnetic and inertial (accelerometry). Finally, markerless systems are adequate for free, unobstructive motion analysis since no attachment is carried by athletes. However, more sensors and sophisticated signal processing must be used to increase the expected level of accuracy.En el ámbito del rendimiento deportivo, el objetivo de la captura de movimiento es seguir y registrar el movimiento humano de deportistas para analizar su condición física, rendimiento, técnica y el origen, prevención y rehabilitación de lesiones. En este artículo, se realiza una revisión sistemática de los últimos avances en sistemas de captura de movimiento para el análisis del rendimiento deportivo. Para ello, se buscaron palabras clave en estudios publicados en los últimos cuatro años en las bases de datos electrónicas ISI Web of Knowledge, Scopus, PubMed y SPORTDiscus, dando lugar a 892 registros. Tras borrar duplicados y análisis del resto, se seleccionaron 81 artículos de revista, distribuidos en 53 registros para sistemas ópticos, 15 para sistemas no ópticos y 13 para sistemas sin marcadores. Los registros se clasificaron según las categorías: análisis biomecánico, validación de nuevos sistemas y mejora del rendimiento. Aunque los sistemas ópticos son los sistemas de referencia por su precisión, el coste del equipamiento y el tiempo invertido en la captura y postprocesado ha llevado a los investigadores a probar otras tecnologías. En primer lugar, los sistemas no ópticos se basan en adherir sensores a zonas corporales para mandar su información espacial a un ordenador mediante distintas tecnologías, tales como electromagnética y inercial (acelerometría). Finalmente, los sistemas sin marcadores permiten un análisis del movimiento sin restricciones ya que los deportistas no llevan adherido ningún elemento. Sin embargo, se necesitan más sensores y un procesado de señal avanzado para aumentar el nivel de precisión necesario

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Intelligent fall detection system for eldercare

Fall among elders is a main reason to cause accidental death among the population over the age 65 in United States. The fall detection methods have been brought into scene by implemented on different fall monitoring devices. For the advantages in privacy protection and non-invasive, independent of light, I design the fall detection system based on Doppler radar sensor. This dissertation explores different Doppler radar sensor configurations and positioning in both of the lab and real senior home environment, signal processing and machine learning algorithms. Firstly, I design the system based on the data collected with three configurations: two floor radars, one ceiling and one wall radars, one ceiling and one floor radars in lab. The performance of the sensor positioning and features are evaluated with classifiers: support vector machine, nearest neighbor, naïve Bayes, hidden Markov model. In the real senior home, I investigate the system by evaluating the detection variances caused by training dataset due to the variable subjects and environment settings. Moreover, I adjust the automatic fall detection system for the actual retired community apartment. I examine different features: Mel-frequency cepstral coefficients (MFCCs), local binary patterns (LBP) and the combined version of features with RELIEF algorithm. I also improve the detection performance with both pre-screener and features selection. I fuse the radar fall detection system with motion sensors. I develop a standalone fall detection system and generate a result to display on a designed webpage