An Intelligent Ambulatory Fall Risk Assessment Method Based on the Detection of Compensatory Balance Reactions and Environmental Factors

Falls in older adults are a critical public health problem worldwide and impact one in three older adults at least once each year. In addition to physical consequences (e.g., hip fracture) falls can lead to negative psychological outcomes, such as depression. Fall risk assessment (FRA) is the initial step for fall prevention programs and interventions. In particular, clinicians aim to understand what factors put older adults at high risk of falling to inform the selection and timing of fall prevention interventions (e.g., strengthening programs). These risk factors are generally categorized as intrinsic or biological (e.g., gait and balance disorders) and extrinsic or environmental (e.g., slippery surfaces). While supervised FRAs, including performance-based (e.g., Timed up and Go) and instrumented methods (e.g., motion capture systems), capable of quantifying intrinsic risks have advanced significantly, falls still remain a major priority in geriatric medicine and public health. This can be due to the Hawthorne effect, the heterogeneous nature of older adults' health, lifestyle, and behaviors, and the complex, multifactorial etiology of falls. To address the limitation of supervised FRAs, a growing body of literature has focused on wearable sensor-based methods for free-living (or ambulatory) FRA. These studies, reviewed in Chapter 2, investigated the relationships between free-living digital biomarkers (FLDBs) extracted from wearable sensors data (generally, inertial data) and the frequency of prospective/retrospective falls in older adults. However, many FLDBs exhibited inconsistent fall predictive powers across studies, indicating they may not be stable in distinguishing fall-prone individuals. Moreover, the relationships between falls and free-living dynamic postural control measures, such as step width and the frequency of naturally-occurring compensatory balance reactions (CBRs), have yet to be investigated in depth. Considering controlled studies reported balance impairment as one of the strongest risk factors for falls, the investigation of balance-related FLDBs may lead to more stable risk assessments and provide new insights into fall prevention in older adults. Although gait-related FLDBs extracted from inertial data can be impacted by both intrinsic and environmental factors, their respective impacts have not been differentiated by the majority of free-living FRA methods. This may lead to the ambiguous interpretation of the subsequent FLDBs, and less precise intervention strategies to prevent falls. A context-aware free-living FRA would elucidate the interplay between intrinsic and environmental risk factors and clarifies their respective impacts on fall predictive powers of FLDBs. This may subsequently enable clinicians to target more specific intervention strategies including environmental modification (e.g., eliminating tripping hazards) and/or rehabilitation interventions (e.g., training to negotiate stairs/transitions). This doctoral thesis aims to address the aforementioned research gaps by proposing multiple machine learning frameworks and incorporating an egocentric camera along with wearable inertial measurement units (IMUs). Chapter 3 discusses the development of random forest models to differentiate between normal gait episodes and multidirectinoal CBRs (e.g, slip-like, trip-like, sidestep) elicited by a perturbation treadmill in controlled conditions in healthy young adults, where the CBR detection model achieved the overall accuracy of ~96%. This chapter established the infrastructure for Chapter 4, where a validation study was performed to detect older adults' CBRs under free-living conditions. Random forest models were trained on independent/unseen datasets curated from multiple sources, including perturbation treadmill CBRs. By investigating 11 fallers' and older non-fallers' free-living criterion standard data, 8 naturally-occurring CBRs, i.e., 7 trips (self-reported using a wrist-mounted voice-recorder) and 1 hit/bump (verified using egocentric vision data) were localized in the corresponding trunk-mounted IMU data. A subset of models differentiated between naturally-occurring CBRs and free-living activities with high sensitivity (100%) and specificity (~99%) suggesting that accurate detection of naturally-occurring CBRs is feasible. Moreover, to address the limitations of IMUs in terms of the estimation of step width in free-living conditions, Chapter 5 presents a novel markerless deep learning-based model to obtain gait patterns by localizing feet in the egocentric vision data captured by a waist-mounted camera. With the aim of improving the interpretability of gait-related FLDBs and investigating the impact of environment on older adults' gait, Chapter 6 proposes a vision-based framework to automatically detect the most common level walking surfaces. Using a belt-mounted camera and IMUs worn by fallers and non-fallers (mean age 73.6 yrs), a unique dataset was acquired (a subset of Multimodal Ambulatory Gait and Fall Risk Assessment in the Wild (MAGFRA-W) dataset). A series of ConvNets were developed: EgoPlaceNet categorizes frames into indoor and outdoor; and EgoTerrainNet (with outdoor and indoor versions) detects the enclosed terrain type in patches. EgoPlaceNet detected outdoor and indoor scenes in MAGFRA-W with 97.36% and 95.59% (leave-one-subject-out) accuracies, respectively. EgoTerrainNet-Indoor and -Outdoor achieved high detection accuracies for pavement (87.63%), foliage (91.24%), gravel (95.12%), and high-friction materials (95.02%), which indicate the models' high generalizabiliy. Overall, promising results encourage the integration of wearable cameras and machine learning approaches to complement IMU-based free-living FRAs, towards stable context-aware FLDBs for fall prevention in older adults. Implications for further research to examine the relationships between naturally-occurring CBRs and fall risk, and clinical applications are discussed

Gait monitoring: from the clinics to the daily life

Monitoring of gait in daily living allows a quantitative analysis of walking in unrestricted conditions, with many potential clinical applications. This thesis aims at addressing the limitations that still hinder the wider adoption of this approach in clinical practice, providing healthcare professionals and researchers new tools which may impact on current gait assessment procedures and improve the treatment of many diseases leading to – or generated by – mobility impairments. The thesis comprises four experimental sections: Accuracy of commercially-available devices. Step detection accuracy in currently available physical activity monitors was assessed in healthy individuals. The best performing device was then tested in multiple sclerosis patients, showing reliability but highly speed-dependent accuracy. These findings suggest that a short set of tests performed in controlled conditions could inform researchers before starting unsupervised monitoring of gait in patients. Differences between laboratory and free-living gait parameters. The study assessed the accuracy of two algorithms for gait event detection, and provided normative values of gait temporal parameters for healthy subjects in different environments and types of walking. A pilot study toward clinical application. This pilot study compared laboratory based tests with daily living assessment of gait features in multiple sclerosis patients. Results provided clear evidence that in this population clinical gait tests might not represent typical gait patterns of daily living. Analysis of free-living walking in patients with Diabetes. A systematic review is presented looking for evidence of the effectiveness of walking as physical activity to reduce inflammation. Then, cadence and step duration variability are examined during free-living walking in a group of patients with diabetes. This thesis systematically highlighted potential and actual limitations in the use of wearable sensors for gait monitoring in daily life, providing clear practical indications and normative values which are essential for the widespread informed and effective clinical adoption of this technology

7-degree-of-freedom hybrid-manipulator exoskeleton for lower-limb motion capture

Lower-limb exoskeletons are wearable robotic systems with a kinematic structure closely matching that of the human leg. In part, this technology can be used to provide clinical assessment and improved independent-walking competency for people living with the effects of stroke, spinal cord injury, Parkinson’s disease, multiple sclerosis, and sarcopenia. Individually, these demographics represent approximately: 405 thousand, 100 thousand, 67.5 thousand, 100 thousand, and 5.9 million Canadians, respectively. Key shortcomings in the current state-of-the-art are: restriction on several of the human leg’s primary joint movements, coaxial joint alignments at the exoskeleton-human interface, and exclusion of well-suited parallel manipulator components. A novel exoskeleton design is thus formulated to address these issues while maintaining large ranges of joint motion. Ultimately, a single-leg unactuated prototype is constructed for seven degree-of-freedom joint angle measurements; it achieves an extent of motion-capture accuracy comparable to a commercial inertial-based system during three levels of human mobility testing

Body sensor networks: smart monitoring solutions after reconstructive surgery

Advances in reconstructive surgery are providing treatment options in the face of major trauma and cancer. Body Sensor Networks (BSN) have the potential to offer smart solutions to a range of clinical challenges. The aim of this thesis was to review the current state of the art devices, then develop and apply bespoke technologies developed by the Hamlyn Centre BSN engineering team supported by the EPSRC ESPRIT programme to deliver post-operative monitoring options for patients undergoing reconstructive surgery. A wireless optical sensor was developed to provide a continuous monitoring solution for free tissue transplants (free flaps). By recording backscattered light from 2 different source wavelengths, we were able to estimate the oxygenation of the superficial microvasculature. In a custom-made upper limb pressure cuff model, forearm deoxygenation measured by our sensor and gold standard equipment showed strong correlations, with incremental reductions in response to increased cuff inflation durations. Such a device might allow early detection of flap failure, optimising the likelihood of flap salvage. An ear-worn activity recognition sensor was utilised to provide a platform capable of facilitating objective assessment of functional mobility. This work evolved from an initial feasibility study in a knee replacement cohort, to a larger clinical trial designed to establish a novel mobility score in patients recovering from open tibial fractures (OTF). The Hamlyn Mobility Score (HMS) assesses mobility over 3 activities of daily living: walking, stair climbing, and standing from a chair. Sensor-derived parameters including variation in both temporal and force aspects of gait were validated to measure differences in performance in line with fracture severity, which also matched questionnaire-based assessments. Monitoring the OTF cohort over 12 months with the HMS allowed functional recovery to be profiled in great detail. Further, a novel finding of continued improvements in walking quality after a plateau in walking quantity was demonstrated objectively. The methods described in this thesis provide an opportunity to revamp the recovery paradigm through continuous, objective patient monitoring along with self-directed, personalised rehabilitation strategies, which has the potential to improve both the quality and cost-effectiveness of reconstructive surgery services.Open Acces

A survey on wearable sensor modality centred human activity recognition in health care

Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR

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

Three-Dimensional Measurement of Spinal Kinematics and Whole-Body Activity Recognition

Back pain is one of the leading causes of disability, being the second largest contributor to work days missed, and sixth largest disability when expressed in terms of an overall burden measured in disability-adjusted life years. Back pain is a large economic burden, where indirect costs from work days missed far outweigh the direct costs due to treatment. As such, it is economically better to prevent back pain from occurring, rather than treating it after the onset of pain. Some risk factors of back pain which can be monitored to help in the prevention of pain include poor posture and prolonged sedentary behaviour. Inactivity, being similar to prolonged sedentary behaviour, is also a risk factor for some of the major non-communicable diseases responsible for death including heart diseases, stroke, breast and colon cancer, and diabetes. The aims of the thesis were to: 1) compare a number of commonly used measurement systems, including a low-cost wearable sensor, in their ability to measure motion typically seen in the human spine; 2) develop an activity classification model capable of predicting everyday activities including standing, sitting, lying, and walking; 3) create a new, inexpensive device that can simultaneously track user spine posture/kinematics and activity; and 4) validate the device to have accuracy within ±5° for spine posture, and an average positive activity classification rate of 90% or above. This research demonstrates the accuracy of a low-cost wearable sensor in its ability to track motion similar to that of the human spine under typical conditions and compare this to more expensive systems. Using two accelerometers and machine learning, a new activity recognition model was created with the ability to track 13 distinct activities commonly used in daily living, being: standing, sitting, prone, supine, right-side, and left-side lying, walking, jogging, jumping, stair ascending, stair descending, walking on an incline, and transitions. From this new knowledge, a new concept inertial-sensor-based device was created with the capabilities of measuring spinal kinematics and whole-body activity tracking. The device has been developed to measure spinal motions with mean errors of ±2.5°, and therefore meeting the aim to have an accuracy within ±5°, while also showing that the more superior the position on the spine an inertial sensor is placed, the higher the errors in measurement. The device can also predict standing, sitting, lying, and walking with an average accuracy of 95.6%, and therefore above the desired accuracy of 90%. When including all activities, the classifier has an average accuracy of 90.3%. To reduce the global effect of back pain, the developed device has the capabilities to aid in the prevention, management, and rehabilitation of back pain by focussing on two risk factors: poor posture and inactivity. For use in this research, the definition of a good posture is one that compromises between minimising spinal load and minimise muscle activity, therefore a poor posture is one that doesn’t adhere to this requirement which could significantly increase the risk of the onset of back pain. For widespread use, the device created in this research has been developed to be as inexpensive as possible. To meet these goals, the future work of the device has been outlined, including size and cost reduction, as well as increasing the aesthetic appeal, thus making it a more appealing product to the general population.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Instrumentation of a cane to detect and prevent falls

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)The number of falls is growing as the main cause of injuries and deaths in the geriatric community. As a result, the cost of treating the injuries associated with falls is also increasing. Thus, the development of fall-related strategies with the capability of real-time monitoring without user restriction is imperative. Due to their advantages, daily life accessories can be a solution to embed fall-related systems, and canes are no exception. Moreover, gait assessment might be capable of enhancing the capability of cane usage for older cane users. Therefore, reducing, even more, the possibility of possible falls amongst them. Summing up, it is crucial the development of strategies that recognize states of fall, the step before a fall (pre-fall step) and the different cane events continuously throughout a stride. This thesis aims to develop strategies capable of identifying these situations based on a cane system that collects both inertial and force information, the Assistive Smart Cane (ASCane). The strategy regarding the detection of falls consisted of testing the data acquired with the ASCane with three different fixed multi-threshold fall detection algorithms, one dynamic multi-threshold and machine learning methods from the literature. They were tested and modified to account the use of a cane. The best performance resulted in a sensitivity and specificity of 96.90% and 98.98%, respectively. For the detection of the different cane events in controlled and real-life situations, a state-of-the-art finite-state-machine gait event detector was modified to account the use of a cane and benchmarked against a ground truth system. Moreover, a machine learning study was completed involving eight feature selection methods and nine different machine learning classifiers. Results have shown that the accuracy of the classifiers was quite acceptable and presented the best results with 98.32% of overall accuracy for controlled situations and 94.82% in daily-life situations. Regarding pre-fall step detection, the same machine learning approach was accomplished. The models were very accurate (Accuracy = 98.15%) and with the implementation of an online post-processing filter, all the false positive detections were eliminated, and a fall was able to be detected 1.019s before the end of the corresponding pre-fall step and 2.009s before impact.O número de quedas tornou-se uma das principais causas de lesões e mortes na comunidade geriátrica. Como resultado, o custo do tratamento das lesões também aumenta. Portanto, é necessário o desenvolvimento de estratégias relacionadas com quedas e que exibam capacidade de monitorização em tempo real sem colocar restrições ao usuário. Devido às suas vantagens, os acessórios do dia-a-dia podem ser uma solução para incorporar sistemas relacionados com quedas, sendo que as bengalas não são exceção. Além disso, a avaliação da marcha pode ser capaz de aprimorar a capacidade de uso de uma bengala para usuários mais idosos. Desta forma, é crucial o desenvolvimento de estratégias que reconheçam estados de queda, do passo anterior a uma queda e dos diferentes eventos da marcha de uma bengala. Esta dissertação tem como objetivo desenvolver estratégias capazes de identificar as situações anteriormente descritas com base num sistema incorporado numa bengala que coleta informações inerciais e de força, a Assistive Smart Cane (ASCane). A estratégia referente à deteção de quedas consistiu em testar os dados adquiridos através da ASCane com três algoritmos de deteção de quedas (baseados em thresholds fixos), com um algoritmo de thresholds dinâmicos e diferentes classificadores de machine learning encontrados na literatura. Estes métodos foram testados e modificados para dar conta do uso de informação adquirida através de uma bengala. O melhor desempenho alcançado em termos de sensibilidade e especificidade foi de 96,90% e 98,98%, respetivamente. Relativamente à deteção dos diferentes eventos da ASCane em situações controladas e da vida real, um detetor de eventos da marcha foi e comparado com um sistema de ground truth. Além disso, foi também realizado um estudo de machine learning envolvendo oito métodos de seleção de features e nove classificadores diferentes de machine learning. Os resultados mostraram que a precisão dos classificadores foi bastante aceitável e apresentou, como melhores resultados, 98,32% de precisão para situações controladas e 94.82% para situações do dia-a-dia. No que concerne à deteção de passos pré-queda, a mesma abordagem de machine learning foi realizada. Os modelos foram precisos (precisão = 98,15%) e com a implementação de um filtro de pós-processamento, todas as deteções de falsos positivos foram eliminadas e uma queda foi passível de ser detetada 1,019s antes do final do respetivo passo de pré-queda e 2.009s antes do impacto

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields