Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Instrumentation and validation of a robotic cane for transportation and fall prevention in patients with affected mobility

Dissertação de mestrado integrado em Engenharia Física, (especialização em Dispositivos, Microssistemas e Nanotecnologias)O ato de andar é conhecido por ser a forma primitiva de locomoção do ser humano, sendo que este traz muitos benefícios que motivam um estilo de vida saudável e ativo. No entanto, há condições de saúde que dificultam a realização da marcha, o que por consequência pode resultar num agravamento da saúde, e adicionalmente, levar a um maior risco de quedas. Nesse sentido, o desenvolvimento de um sistema de deteção e prevenção de quedas, integrado num dispositivo auxiliar de marcha, seria essencial para reduzir estes eventos de quedas e melhorar a qualidade de vida das pessoas. Para ultrapassar estas necessidades e limitações, esta dissertação tem como objetivo validar e instrumentar uma bengala robótica, denominada Anti-fall Robotic Cane (ARCane), concebida para incorporar um sistema de deteção de quedas e um mecanismo de atuação que possibilite a prevenção de quedas, ao mesmo tempo que assiste a marcha. Para esse fim, foi realizada uma revisão do estado da arte em bengalas robóticas para adquirir um conhecimento amplo e aprofundado dos componentes, mecanismos e estratégias utilizadas, bem como os protocolos experimentais, principais resultados, limitações e desafios em dispositivos existentes. Numa primeira fase, foi estipulado o objetivo de: (i) adaptar a missão do produto; (ii) estudar as necessidades do consumidor; e (iii) atualizar as especificações alvo da ARCane, continuação do trabalho de equipa, para obter um produto com design e engenharia compatível com o mercado. Foi depois estabelecida a arquitetura de hardware e discutidos os componentes a ser instrumentados na ARCane. Em seguida foram realizados testes de interoperabilidade a fim de validar o funcionamento singular e coletivo dos componentes. Relativamente ao controlo de movimento, foi desenvolvido um sistema inovador, de baixo custo e intuitivo, capaz de detetar a intenção do movimento e de reconhecer as fases da marcha do utilizador. Esta implementação foi validada com seis voluntários saudáveis que realizaram testes de marcha com a ARCane para testar sua operabilidade num ambiente de contexto real. Obteve-se uma precisão de 97% e de 90% em relação à deteção da intenção de movimento e ao reconhecimento da fase da marcha do utilizador. Por fim, foi projetado um método de deteção de quedas e mecanismo de prevenção de quedas para futura implementação na ARCane. Foi ainda proposta uma melhoria do método de deteção de quedas, de modo a superar as limitações associadas, bem como a proposta de dispositivos de deteção a serem implementados na ARCane para obter um sistema completo de deteção de quedas.The act of walking is known to be the primitive form of the human being, and it brings many benefits that motivate a healthy and active lifestyle. However, there are health conditions that make walking difficult, which, consequently, can result in worse health and, in addition, lead to a greater risk of falls. Thus, the development of a fall detection and prevention system integrated with a walking aid would be essential to reduce these fall events and improve people quality of life. To overcome these needs and limitations, this dissertation aims to validate and instrument a cane-type robot, called Anti-fall Robotic Cane (ARCane), designed to incorporate a fall detection system and an actuation mechanism that allow the prevention of falls, while assisting the gait. Therefore, a State-of-the-Art review concerning robotic canes was carried out to acquire a broad and in-depth knowledge of the used components, mechanisms and strategies, as well as the experimental protocols, main results, limitations and challenges on existing devices. On a first stage, it was set an objective to (i) enhance the product's mission statement; (ii) study the consumer needs; and (iii) update the target specifications of the ARCane, extending teamwork, to obtain a product with a market-compatible design and engineering that meets the needs and desires of the ARCane users. It was then established the hardware architecture of the ARCane and discussed the electronic components that will instrument the control, sensory, actuator and power units, being afterwards subjected to interoperability tests to validate the singular and collective functioning of cane components altogether. Regarding the motion control of robotic canes, an innovative, cost-effective and intuitive motion control system was developed, providing user movement intention recognition, and identification of the user's gait phases. This implementation was validated with six healthy volunteers who carried out gait trials with the ARCane, in order to test its operability in a real context environment. An accuracy of 97% was achieved for user motion intention recognition and 90% for user gait phase recognition, using the proposed motion control system. Finally, it was idealized a fall detection method and fall prevention mechanism for a future implementation in the ARCane, based on methods applied to robotic canes in the literature. It was also proposed an improvement of the fall detection method in order to overcome its associated limitations, as well as detection devices to be implemented into the ARCane to achieve a complete fall detection system

Backward Walking: A Novel Marker Of Fall Risk, Cognitive Dysfunction, And Myelin Damage In Persons With Multiple Sclerosis

Multiple sclerosis (MS) is a progressive, neurologic disease of the central nervous system that causes debilitating motor, sensory and cognitive impairments. As a result, persons with MS are at an increased risk for falls and falls represent a serious public health concern for the MS population. The current clinical measures used to assess fall risk in MS patients lack sensitivity and predictive validity for falls and are limited in their ability to capture to multiple functional domains (i.e., motor, sensory, cognitive and pathological domains) that are impaired by MS. Backward walking sensitively detects falls in the elderly and other neurologic diseases. However, backward walking and falls has never been explored in the MS population and the underlying reasons as to why backward walking sensitively detects falls remains unknown. Identification of a quick, simply and clinically feasible fall risk measures related to multiple functions impacted by MS and related to fall risk, which can detect falls before they occur is critical for fall prevention and timely and targeted intervention. Therefore, this dissertation examines backward walking as a novel marker of fall risk and its cognitive and pathological underpinnings to support its clinical utility. Our results indicate that backward walking is a sensitive marker of fall risk in the MS population, regardless of co-morbid cognitive deficits, and that examining underlying brain regions likely to contribute to backward walking performance including the corticospinal tract, corpus callosum and cerebellum, with neuroimaging tools sensitive to myelin (i.e., Myelin Water Imaging) demonstrate potential to identify underlying mechanisms of backward walking performance in the MS population. This work is the critical first step in establishing backward walking as a sensitive marker of fall risk for the MS population and leads the way to more personalized fall prevention therapies and interventions to improve clinical outcomes and decrease fall rates in the MS population

Employing multi-modal sensors for personalised smart home health monitoring.

Smart home systems are employed worldwide for a variety of automated monitoring tasks. FITsense is a system that performs personalised smart home health monitoring using sensor data. In this thesis, we expand upon this system by identifying the limits of health monitoring using simple IoT sensors, and establishing deployable solutions for new rich sensing technologies. The FITsense system collects data from FitHomes and generates behavioural insights for health monitoring. To allow the system to expand to arbitrary home layouts, sensing applications must be delivered while relying on sparse "ground truth" data. An enhanced data representation was tested for improving activity recognition performance by encoding observed temporal dependencies. Experiments showed an improvement in activity recognition accuracy over baseline data representations with standard classifiers. Channel State Information (CSI) was chosen as our rich sensing technology for its ambient nature and potential deployability. We developed a novel Python toolkit, called CSIKit, to handle various CSI software implementations, including automatic detection for off-the-shelf CSI formats. Previous researchers proposed a method to address AGC effects on COTS CSI hardware, which we tested and found to improve correlation with a baseline without AGC. This implementation was included in the public release of CSIKit. Two sensing applications were delivered using CSIKit to demonstrate its functionality. Our statistical approach to motion detection with CSI data showed a 32% increase in accuracy over an infrared sensor-based solution using data from 2 unique environments. We also demonstrated the first CSI activity recognition application on a Raspberry Pi 4, which achieved an accuracy of 92% with 11 activity classes. An application was then trained to support movement detection using data from all COTS CSI hardware. This was combined with our signal divider implementation to compare CSI wireless and sensing performance characteristics. The IWL5300 exhibited the most consistent wireless performance, while the ESP32 was found to produce viable CSI data for sensing applications. This establishes the ESP32 as a low-cost high-value hardware solution for CSI sensing. To complete this work, an in-home study was performed using real-world sensor data. An ESP32-based CSI sensor was developed to be integrated into our IoT network. This sensor was tested in a FitHome environment to identify how the data from our existing simple sensors could aid sensor development. We performed an experiment to demonstrate that annotations for CSI data could be gathered with infrared motion sensors. Results showed that our new CSI sensor collected real-world data of similar utility to that collected manually in a controlled environment

Identification of falls risk factors in community-dwelling older adults: validation of the Comprehensive Falls Risk Screening Instrument

Identifying risk factors and those at risk for falls is necessary. The first purpose of the dissertation was to validate the Comprehensive Falls Risk Screening Instrument (CFRSI) that weights falls risk factors and includes the subscale scores of history, physical, vision, medication, and environment, and a total falls risk score. The CFRSI total falls risk score was compared to subscale scores, physical activity, physical function, health-related quality of life (HRQL), and history of falls (Study 1). The second purpose of the dissertation was to determine associations between the CFRSI total falls risk score, race, education, and income (Study 2). Data were collected at falls risk screenings conducted at 10 community organizations with 286 older adults (M age=74.2 years, SD=10.0, 75.9% female, 52.9% White/Caucasian, 52.4% low-income status, and 43.1% low educational level). The total falls risk score was associated with all risk subscale scores (r=.25, p\u3c.01 to r=.69, p\u3c.01), total physical activity score (r=-.30, p\u3c.01), total physical function score (r=.30, p\u3c.01), and total HRQL scores (r=-.44, p\u3c.01 to r=-.24, p=.03). Fallers (n=90) had higher total falls risk scores (M=41.03, SD=9.38) than non-fallers (n=188; M=34.06, SD=10.05), t(276)=5.53, p\u3c.001). Discriminant function analysis indicated the most important predictor of falling status (i.e., fallers and non-fallers) was the history risk score (r=.96). A 2x2x2 factorial ANOVA only revealed a significant main effect for education (F[1,205]=10.19, p=.002), indicating that the total falls risk score was greater for participants with a lower educational level (M=41.1) than for those with a higher educational level (M=34.5). ANCOVA revealed that individuals with low-income reported higher falls risk scores (M=39.2) than individuals with high-income (M=34.5) when controlling for race (F[1,204]=10.4, p=.001,ç2=.05). There were no significant differences between fallers and non-fallers by education (÷2[1,N=262]=.03, p=.86) or income (÷2[1,N=212]=.38, p=.54), but there were differences by race (÷2[1,N=267]=6.44, p=.0). White/Caucasians (63.2%) were more likely to fall than African American/Black/Others (36.8%). Results provide evidence of the construct validity of the CFRSI and that sociodemographic factors such as education, income, and race are important when identifying older adults at risk for falls, determining applicability of falls risk screening instruments, and implementing falls reduction programs

Design revolutions: IASDR 2019 Conference Proceedings. Volume 4: Learning, Technology, Thinking

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 4 includes papers from Learning, Technology and Thinking tracks of the conference

Fall risk in older adults with hip osteoarthritis : decreasing risk through education and aquatic exercise

Purpose: The primary purpose of this project was to determine the effect of aquatic exercise and aquatic exercise combined with an education group program on decreasing both psychosocial and physical fall risk factors in community-dwelling older adults with hip osteoarthritis (OA). Secondary purposes were to 1) describe fall risk, history and nature of falls and near-falls in older adults with hip OA, 2) determine the association of the timed up and go test (TUG) to history of falls and near-falls, 4) explore the relationship of both psychosocial and physical factors to history of falls and near-falls, and 5) evaluate the role of falls-efficacy in predicting balance performance. Methods: Participants were recruited from the community and screened for presence of hip osteoarthritis and fall risk. Baseline fall history and a battery of measures for balance, muscle strength, functional ability and falls-efficacy were administered. Participants were then randomly assigned to one of three groups: Aquatic Exercise, Aquatic Exercise and Education or a Control Group. The interventions were twice per week for 11 weeks. Fall risk factors were measured after 11 weeks. Study 1 described history of falls and near-falls and evaluated the association of the TUG screening test with fall and near-fall history. Study 2 summarized the relationships of physical and psychosocial fall risk factors and identified the primary predictors of fall risk, based on associations with fall history. Study 3 evaluated the randomized controlled clinical trial comparing the impact of the interventions (aquatic exercise and education) on fall risk outcomes. Results: Older adults with hip OA reported a high frequency of falls and near-falls. The TUG, using a cut-off score of 10 sec., was associated with frequent near-fall history. There was a strong association of frequent near-falls to history of actual falls, with the association increasing 7-fold if lower falls-efficacy was present. Falls-efficacy was also an independent predictor of balance impairment. Screening for history of near-falls and falls-efficacy may be important in predicting risk of future falls. The combination of Aquatic Exercise and Education improved falls-efficacy and functional mobility compared to Aquatic Exercise only or no intervention. Aquatic Exercise on its own was not effective in decreasing fall risk factors or improving falls-efficacy. Significance of Findings: The accumulation of both physical and psychosocial risk factors in older adults with hip OA increases their vulnerability to falls and injury. Fall prevention programs for this population should be designed to include both exercise and education to address falls-efficacy and physical fall risk factors

Understanding the Influence of Fear of Falling on Clinical Balance Control - Efforts in Fall Prediction and Prevention

Introduction: A review of the literature shows that standard clinical balance measures do not adequately predict fall risk in community-dwelling older individuals. There is significant evidence demonstrating the interactions of fear, anxiety, and confidence with the control of standing posture. Little is known however about the nature of this relationship under more challenging balance conditions, particularly in the elderly. The primary purpose of this work was to evaluate the relationship between fear of falling, clinical balance measures and fall-risk. Methods: Three studies were conducted evaluating the effects of postural threat (manipulated by support surface elevation) and/or cognitive loading (working memory secondary task) on clinical balance performance and task-specific psychological measures. Predictive and construct validity as well as test-retest reliability was evaluated for measures used to assess fear of falling and related psychological constructs . Results: Postural threat resulted in reduced balance confidence and perceived stability as well as increased state anxiety and fear of falling. These changes were significantly correlated to decrements in performance of clinical balance tasks. Neither standard clinical scales of balance and mobility nor generalized psychological measures, alone or in combination, could predict falls in community-dwelling elderly. However, combined scores on selected challenging clinical balance tasks could significantly predict falls. Furthermore, improved predictive precision resulted from having these tasks performed under combined postural threat and cognitive loading. Finally, the inclusion of task-specific psychological measures resulted in further improvements to predictive precision. Psychological measures demonstrated fair to excellent test-retest reliability in both healthy young and independent-living older individuals. Conclusions: Clinical balance tasks performed under more challenging conditions likely better reflect everyday experiences in which a fall is likely to occur. Incorporating easy-to-administer task-specific psychological evaluations and self-reported health estimates with clinical balance assessments might improve the likelihood of correctly identifying community-dwelling individuals at risk for falls. Improved estimates of fall-risk may lead to a reduction in the number of falls experienced in this population, thereby reducing the significant burden of fall-related hospitalizations, treatments and rehabilitation on the individual, families and health care system