Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

The role of mobile technology for fall risk assessment for individuals with multiple sclerosis

Multiple Sclerosis (MS) is a chronic, progressive neurogenerative disease that affects one million people in the United States (Wallin et al., 2019). Common MS symptoms include impaired coordination, poor walking and balance, and fatigue, and these symptoms put people with MS (pwMS) at a higher risk for falls (Cameron & Nilsagard, 2018). Falls are highly prevalent among pwMS and can result in detrimental consequences including bone fractures and even death (Matsuda et al., 2011). To prevent falls and fall related injuries, it is important to first assess for multiple risk factors and then intervene through targeted treatments (Palumbo et al., 2015). Fall risk can be assessed through self-report measures, clinical performance tests, or with technology such as force plates and motion capture systems (Kanekar & Aruin, 2013). However, clinicians have time constraints, technology is expensive, and trained personnel is needed. Moreover, due to the COVID-19 pandemic, access to in-person clinical visits is limited. As a result, pwMS may not receive fall risk screening and remain vulnerable to fall related injuries. Mobile technology offers a solution to increase access to fall risk screening using an affordable, ubiquitous, and portable tool (Guise et al., 2014; Marrie et al., 2019). Therefore, the overarching goal of this study was to develop a usable fall risk health application (app) for pwMS to self-assess their fall risk in the home setting. Four studies were performed: 1) smartphone accelerometry was tested to measure postural control in pwMS; 2) a fall risk algorithm was developed for a mobile health app; 3) a fall risk app, Steady-MS, was developed and its usability was tested; and 4) the feasibility of home-based procedures for using Steady-MS was determined. Results suggest that smartphone accelerometry can assess postural control in pwMS. This information was used to develop an algorithm to measure overall fall risk in pwMS and was then incorporated into Steady-MS. Steady-MS was found to be usable among MS users and feasible to use in the home setting. The results from this project demonstrate that pwMS can independently assess their fall risk with Steady-MS in their homes. For the first time, pwMS are equipped to self-assess their fall risk and can monitor and manage their risk. Home-based assessments also opens the potential to offer individualized and targeted treatments to prevent falls. Ultimately, Steady-MS increases access to home-based assessments to reduce falls and improve functional independence for those with MS

The Emerging Wearable Solutions in mHealth

The marriage of wearable sensors and smartphones have fashioned a foundation for mobile health technologies that enable healthcare to be unimpeded by geographical boundaries. Sweeping efforts are under way to develop a wide variety of smartphone-linked wearable biometric sensors and systems. This chapter reviews recent progress in the field of wearable technologies with a focus on key solutions for fall detection and prevention, Parkinson’s disease assessment and cardiac disease, blood pressure and blood glucose management. In particular, the smartphone-based systems, without any external wearables, are summarized and discussed

Understanding the determinants of independent mobility in older adults

As aging occurs, safely maintaining an active lifestyle is critical for health and independence. Independent mobility is influenced by one’s ability to perform three essential tasks of daily living: transitioning from a seated to standing posture, maintaining upright stance and walking. In spite of the apparent similarities in the predictive utility of these different tasks, there are few studies that have explored the specific relationship between these tasks that define independent mobility within individuals to determine if they reflect unique challenges to control. The thesis focused on two studies to advance understanding of the determinants of independent mobility in older adults. Study 1 explored the association between measures of standing, transitions and walking in 28 older adults. An important element was the assessment using portable low-cost measurement technology (Wii force boards and wearable accelerometers) so that testing could be done in the community. The results of this study revealed the potential importance of sit-to-stand performance as an independent measure of function in older adults. One important outcome was the need for a more detailed measurement of the sit-to-stand task, which is characterized by different phases that have unique control challenges. As a result, Study 2 was designed to evaluate different measurements of the sit-to-stand phases in order to provide a measurement tool that could be used in community and clinical testing. Ground reaction forces were found capable of identifying the different sit-to-stand phases and therefore afford the ability to quantify this behavior using portable technology. Identifying the underlying control mechanisms and relationships between these mechanisms allows clinicians to prescribe targeted and potentially more effective interventions focused on behavior specific control challenges

Investigating sit-to-stand velocity and power to assess functional capacity in older people

Portable inertial measurement units allow movement velocity and power to be measured during functional assessments for older people, providing comprehensive assessment of their functional capacity. This study investigated the reliability of PUSH Band 2.0 velocity and power during a single, five-repetition and thirty-second duration sit-to-stand (STS) in older adults, and the relationship of these data with other tests of functional capacity. Twenty-four older adults (14 female; age: 72±5) attended one familiarisation and two experimental testing sessions. Mean STS velocity and power were measured during both testing sessions. Additionally, dynamometry assessments of maximal voluntary contraction (MVC), rate of torque development (RTD) and muscular endurance were measured in one session, while a functional testing battery was performed in the other session. The level of significance was set at p ≤ 0.05. Velocity and power demonstrated excellent relative reliability for all STS tasks (intraclass correlation coefficient = 0.91-0.98). Single-repetition STS velocity observed moderate absolute reliability (coefficient of variation = 6.5%), while velocity and power during all other STS tasks observed good absolute reliability (CV = 3.2-4.8%). Velocity during one (r = -0.64-0.57), five (r = -0.53-0.46) and thirty-second STS (r = -0.51-0.42) correlated with all functional battery scores. Thirty-second STS velocity correlated with MVC (r = 0.41), but not endurance (r = 0.27-0.36) or RTD (r = 0.24-0.37). Power during one (r = 0.50), five (r = 0.62) and thirty-second STS (r = 0.67) significantly correlated with MVC. No other significant correlations were observed. Mean STS velocity and power should not replace functional capacity assessments. However, these data may assist practitioners to monitor improvements in movement velocity and power following exercise interventions or to quantify reductions in capacity following a period of inactivity

Analysis of Android Device-Based Solutions for Fall Detection

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.Ministerio de Economía y Competitividad TEC2013-42711-

Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments

The use of wearable inertial measurement units to assess gait and balance outcomes related to fall risk among older adults

Due to the prevalence and associated health, social and economic costs of falls among older adults, this thesis originally aimed to identify a more robust and objective way of assessing fall risk factors with the use of wearable inertial measurement units (IMU). However, due to unforeseen circumstances, the direction of the thesis had to be changed. Therefore, the thesis aimed to investigate whether gait and balance outcomes related to fall risk, when measured with wearable IMUs are sensitive to conditions which may replicate clinical and habitual environments. In Study one, a systematic scoping review was conducted to identify characteristic differences between fallers and non-fallers with the use of IMUs. The lower trunk was the most common anatomical location, whilst walking a predetermined distance indoors was the most common test used with IMUs to distinguish between fallers and non-fallers. In Study two, seventeen older and seventeen younger adults performed multiple walking and standing tasks in a laboratory. Older adults had a lower root mean square of the IMU acceleration signal, harmonic ratio and greater step time asymmetry compared to younger adults. The use of a cognitive dual task caused gait to be slower and less symmetrical among older and younger adults. Trunk displacement to quantify trunk sway during quiet standing was greater among older adults and increased as standing conditions became more difficult. Older adults exhibited distinct differences in gait when walking indoors and outdoors. The results of Study two suggested that IMUs may identify differences between older and younger adults regarding walking speed and time to completion of clinical tests, even when a stopwatch could not. In Study three, twenty older and twenty younger adults had IMUs attached to different anatomical locations during waking hours. There were differences in all gait variables when walking supervised in the laboratory and unsupervised in habitual indoor environments for both older and younger adults. There were also large differences in gait variables when walking indoors and outdoors. These results suggest the need for future studies in continuous, outdoor and unsupervised free-living conditions, with regards to fall risk assessments. This thesis demonstrates that gait and balance outcomes related to fall risk, when measured using wearable IMUs, are sensitive to conditions resembling habitual and clinical environments among both older and younger adults. This could prove valuable for the enhancement of future fall risk research

IMUs: validation, gait analysis and system’s implementation

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a prevalent problem in actual society. The number of falls has been increasing greatly in the last fifteen years. Some falls result in injuries and the cost associated with their treatment is high. However, this is a complex problem that requires several steps in order to be tackled. Namely, it is crucial to develop strategies that recognize the mode of locomotion, indicating the state of the subject in various situations, namely normal gait, step before fall (pre-fall) and fall situation. Thus, this thesis aims to develop a strategy capable of identifying these situations based on a wearable system that collects information and analyses the human gait. The strategy consists, essentially, in the construction and use of Associative Skill Memories (ASMs) as tools for recognizing the locomotion modes. Consequently, at an early stage, the capabilities of the ASMs for the different modes of locomotion were studied. Then, a classifier was developed based on a set of ASMs. Posteriorly, a neural network classifier based on deep learning was used to classify, in a similar way, the same modes of locomotion. Deep learning is a technique actually widely used in data classification. These classifiers were implemented and compared, providing for a tool with a good accuracy in recognizing the modes of locomotion. In order to implement this strategy, it was previously necessary to carry out extremely important support work. An inertial measurement units’ (IMUs) system was chosen due to its extreme potential to monitor outpatient activities in the home environment. This system, which combines inertial and magnetic sensors and is able to perform the monitoring of gait parameters in real time, was validated and calibrated. Posteriorly, this system was used to collect data from healthy subjects that mimicked Fs. Results have shown that the accuracy of the classifiers was quite acceptable, and the neural networks based classifier presented the best results with 92.71% of accuracy. As future work, it is proposed to apply these strategies in real time in order to avoid the occurrence of falls.As quedas são um problema predominante na sociedade atual. O número de quedas tem aumentado bastante nos últimos quinze anos. Algumas quedas resultam em lesões e o custo associado ao seu tratamento é alto. No entanto, trata-se de um problema complexo que requer várias etapas a serem abordadas. Ou seja, é crucial desenvolver estratégias que reconheçam o modo de locomoção, indicando o estado do sujeito em várias situações, nomeadamente, marcha normal, passo antes da queda (pré-queda) e situação de queda. Assim, esta tese tem como objetivo desenvolver uma estratégia capaz de identificar essas situações com base num sistema wearable que colete informações e analise a marcha humana. A estratégia consiste, essencialmente, na construção e utilização de Associative Skill Memories (ASMs) como ferramenta para reconhecimento dos modos de locomoção. Consequentemente, numa fase inicial, foram estudadas as capacidades das ASMs para os diferentes modos de locomoção. Depois, foi desenvolvido um classificador baseado em ASMs. Posteriormente, um classificador de redes neuronais baseado em deep learning foi utilizado para classificar, de forma semelhante, os mesmos modos de locomoção. Deep learning é uma técnica bastante utilizada em classificação de dados. Estes classificadores foram implementados e comparados, fornecendo a uma ferramenta com uma boa precisão no reconhecimento dos modos de locomoção. Para implementar esta estratégia, era necessário realizar previamente um trabalho de suporte extremamente importante. Um sistema de unidades de medição inercial (IMUs), foi escolhido devido ao seu potencial extremo para monitorizar as atividades ambulatórias no ambiente domiciliar. Este sistema que combina sensores inerciais e magnéticos e é capaz de efetuar a monitorização de parâmetros da marcha em tempo real, foi validado e calibrado. Posteriormente, este Sistema foi usado para adquirir dados da marcha de indivíduos saudáveis que imitiram quedas. Os resultados mostraram que a precisão dos classificadores foi bastante aceitável e o classificador baseado em redes neuronais apresentou os melhores resultados com 92.71% de precisão. Como trabalho futuro, propõe-se a aplicação destas estratégias em tempo real de forma a evitar a ocorrência de quedas

Fall prevention intervention technologies: A conceptual framework and survey of the state of the art

In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge.The Royal Society, grant Ref: RG13082