Technology for monitoring everyday prosthesis use: a systematic review

BACKGROUND Understanding how prostheses are used in everyday life is central to the design, provision and evaluation of prosthetic devices and associated services. This paper reviews the scientific literature on methodologies and technologies that have been used to assess the daily use of both upper- and lower-limb prostheses. It discusses the types of studies that have been undertaken, the technologies used to monitor physical activity, the benefits of monitoring daily living and the barriers to long-term monitoring. METHODS A systematic literature search was conducted in PubMed, Web of Science, Scopus, CINAHL and EMBASE of studies that monitored the activity of prosthesis-users during daily-living. RESULTS 60 lower-limb studies and 9 upper-limb studies were identified for inclusion in the review. The first studies in the lower-limb field date from the 1990s and the number has increased steadily since the early 2000s. In contrast, the studies in the upper-limb field have only begun to emerge over the past few years. The early lower-limb studies focused on the development or validation of actimeters, algorithms and/or scores for activity classification. However, most of the recent lower-limb studies used activity monitoring to compare prosthetic components. The lower-limb studies mainly used step-counts as their only measure of activity, focusing on the amount of activity, not the type and quality of movements. In comparison, the small number of upper-limb studies were fairly evenly spread between development of algorithms, comparison of everyday activity to clinical scores, and comparison of different prosthesis user populations. Most upper-limb papers reported the degree of symmetry in activity levels between the arm with the prosthesis and the intact arm. CONCLUSIONS Activity monitoring technology used in conjunction with clinical scores and user feedback, offers significant insights into how prostheses are used and whether they meet the user’s requirements. However, the cost, limited battery-life and lack of availability in many countries mean that using sensors to understand the daily use of prostheses and the types of activity being performed has not yet become a feasible standard clinical practice. This review provides recommendations for the research and clinical communities to advance this area for the benefit of prosthesis users

Assessment of Physical Activity in Adults with Progressive Muscle Disease

Introduction: Insufficient physical activity is a major threat to global health. Physical activity benefits peoples’ physical and mental health. The general population, including people living with disabilities and muscle wasting conditions, are recommended to avoid excessive sedentary time and engage in daily activity. Adults with progressive muscle disease experience barriers to physical activity participation, including muscle weakness, fatigue, physical deconditioning, impairment, activity limitations and participation restrictions (including societal and environmental factors), and fear of symptom exacerbation. More research is required to understand the inter-relationship between health and physical activity for adults with progressive muscle disease, particularly non-ambulant people who are under-represented in the existing research literature. Accurate measurement of FITT (frequency, intensity, time, and type of physical activity) is vital for high-quality physical activity assessment. The aim of this thesis was to assess the physical activity of ambulant and non-ambulant adults with progressive muscle disease.Systematic review findings identified various measures used to assess physical activity in adults with muscular dystrophy, including accelerometers, direct observation, heart rate monitors, calorimetry, positioning systems, activity diaries, single scales, interviews and questionnaires. None of the measures identified in the systematic review had well established measurement properties for adults with muscular dystrophy.Patient and public involvement interviews highlighted the importance of inclusive, remote, and technology-facilitated research design, the potential intrusion of direct observations of physical activity, the familiarity of questionnaires for data collection, and practical considerations to ensure wearing an activity monitor was not too burdensome.A feasibility study using multiple methods in 20 ambulant and non-ambulant adults with progressive muscle disease revealed satisfactory acceptability, interpretability, and usability of Fitbit and activity questionnaires, in both paper and electronic formats. During supervised activity tasks, Fitbit was found to have satisfactory criterion validity, reliability, and responsiveness and measurement properties were strengthened using multisensory measurement.An observational, longitudinal study that included 111 ambulant and non-ambulant adults with progressive muscle disease showed that:Activity monitoring had satisfactory validity, reliability and responsiveness using Fitbit, but there was considerable measurement error between Fitbit and the research grade GENEActiv accelerometer. Fitbit thresholds and multiple metrics (including accelerometer and heart rate data extrapolations of FITT) were appropriate for physical activity assessment in ambulant and non-ambulant adults with progressive muscle disease.Activity self-report had unsatisfactory concurrent validity, test-retest reliability, and responsiveness with substantial activity overestimation using the modified International Physical Activity Questionnaire. However, self-report properties were improved when used concurrently with Fitbit.Observed physical activity in adults with progressive muscle disease was generally low with excessive daily sedentary time. Activity frequencies, intensities and durations were lower, and activity types were more domestic, for wheelchair users and during the COVID-19 lockdown. Lower physical activity was significantly associated with greater functional impairment, less cardiorespiratory fitness, worse metabolic health, and lower quality of life. Activity optimisation thresholds and minimal clinically important differences were established.Discussion: The implications of this thesis include guidance for selection of appropriate physical activity measures by clinicians and researchers working with adults with progressive muscle disease. Fitbit is suitable in clinical practice and research for interactive, weekly remote activity monitoring or to support activity self-management and may represent an appropriate compromise between potential underestimation by accelerometry alone, and overestimation by self-report alone. A draft conceptual framework for physical activity measurement was also proposed. It includes frequency, intensity, time, and type of physical activity, and incorporates wider aspects of the physical activity construct, including somatic factors (relating to progressive muscle disease and underlying fitness) and contextual factors (relating to personal, social, and environmental situations). Future research will build on the knowledge gained in this thesis, furthering understanding of the inter-relationships between physical activity, health and wider contexts. Implementation will include testing a remote physical activity optimisation intervention that is inclusive of ambulant and non-ambulant participants, featuring Fitbit self-monitoring with a focus on optimisation of daily activity frequency and regularly interrupting sedentary time.</div

implementazione e confronto di pedometri software non real time

Lo sviluppo dei dispositivi mobili negli ultimi anni ha permesso la creazione di pedometri efficienti. Uno dei problemi principali nella realizzazione dei contapassi `e l’accuratezza e la precisione nei risultati. Il seguente elaborato fornisce un’analisi dettagliata dei vari studi presenti in rete a riguardo. La ricerca ha avuto scopo di riassumere le diverse scelte implementative, confrontandole tra di loro e di risaltare i punti in comune fornendo un’analisi sull’effettiva efficacia di ognuna di esse. Il focus di questo studio si concentrer`a sull’analisi di algoritmi per la rilevazione di passi calcolati non in tempo reale. L’elaborato `e stato diviso in quattro differenti fasi. Durante la prima fase vengono esposti i principali studi e le principali metodologie relative all’argomento appena descritto. Nella seconda fase viene presentata la Tassonomia, cio`e una classificazione ordinata di concetti secondo determinati criteri. Nella terza fase `e stata quindi sviluppata una applicazione Android in cui vengono implementanti gli algoritmi descritti nelle fasi precedenti. Nell’ultima fase viene testata l’applicazione attraverso l’uso di specifici test confrontando tra loro i diversi algoritmi proposti

Novel sedentary behaviour measurement methods: application for self-monitoring in adults

With the introduction of the technological age, increasing mechanisation has led to labour saving devices which have all-but engineered physical activity out of our lives and sedentary behaviour has now become the default behaviour during waking hours. Interventions that previously focused on improving levels of physical activity are now attempting to concurrently increase levels of physical activity and decrease time spent in sedentary behaviour. One method that has shown promise in interventions to increase physical activity and healthy eating in adults is the behaviour change technique of self-monitoring. There is now a robust set of literature indicating self-monitoring as the most promising behaviour change technique in this area. Self-monitoring is tied inherently into the recent rise in wearable technology. These new devices have the ability to track a variety of behavioural and physiological parameters and immediately make the information returnable to the user via connected mobile applications. The potential pervasive nature of these technologies and their use of robust behaviour change techniques could make them a useful tool in interventions to reduce sedentary behaviour. Therefore the overall purpose of this three study dissertation was to identify and validate technology that can self-monitor sedentary behaviour and to determine its feasibility in reducing sedentary behaviour. Study 1 Purpose: The aim of this study was to review the characteristics and measurement properties of currently available self-monitoring devices for sedentary behaviour and/or physical activity. Methods: To identify technologies, four scientific databases were systematically searched using key terms related to behaviour, measurement, and population. Articles published through October 2015 were identified. To identify technologies from the consumer electronic sector, systematic searches of three Internet search engines were also performed through to October 1st, 2015. Results: The initial database searches identified 46 devices and the Internet search engines identified 100 devices yielding a total of 146 technologies. Of these, 64 were further removed because they were currently unavailable for purchase or there was no evidence that they were designed for, had been used in, or could readily be modified for self-monitoring purposes. The remaining 82 technologies were included in this review (73 devices self-monitored physical activity, 9 devices self-monitored sedentary time). Of the 82 devices included, this review identified no published articles in which these devices were used for the purpose of self-monitoring physical activity and/or sedentary behaviour; however, a number of technologies were found via Internet searches that matched the criteria for self-monitoring and provided immediate feedback on physical activity (ActiGraph Link, Microsoft Band, and Garmin Vivofit) and sedentary behaviour (activPAL VT, the LumoBack, and Darma). Conclusions: There are a large number of devices that self-monitor physical activity; however, there is a greater need for the development of tools to self-monitor sedentary time. The novelty of these devices means they have yet to be used in behaviour change interventions, although the growing field of wearable technology may facilitate this to change. Study 2 Purpose: The aim of this study was to examine the criterion and convergent validity of the LumoBack as a measure of sedentary behaviour compared to direct observation, the ActiGraph wGT3X+ and the activPAL under laboratory and free-living conditions in a sample of healthy adults. Methods: In the laboratory experiment, 34 participants wore a LumoBack, ActiGraph and activPAL monitor and were put through seven different sitting conditions. In the free-living experiment, a sub-sample of 12 participants wore the LumoBack, ActiGraph and activPAL monitor for seven days. Validity were assessed using Bland-Altman plots, mean absolute percentage error (MAPE), and intraclass correlation coefficient (ICC). T-test and Repeated Measures Analysis of Variance were also used to determine any significant difference in measured behaviours. Results: In the laboratory setting, the LumoBack had a mean bias of 76.2, 72.1 and -92.3 seconds when compared to direct observation, ActiGraph and activPAL, respectively, whilst MAPE was less than 4%. Furthermore, the ICC was 0.82 compared to the ActiGraph and 0.73 compared to the activPAL. In the free-living experiment, mean bias was -4.64, 8.90 and 2.34 seconds when compared to the activPAL for sedentary behaviour, standing time and stepping time respectively. Mean bias was -38.44 minutes when compared to the ActiGraph for sedentary time. MAPE for all behaviours were 0.75. Conclusion: The LumoBack has acceptable validity and reliability as a measure of sedentary behaviour. Study 3 Purpose: The aim of this study was to explore the use of the LumoBack as a behaviour change tool to reduce sedentary behaviour in adults. Methods: Forty-two participants (≥25 years) who had an iPhone 4S or later model wore the LumoBack without any feedback for one week for baseline measures of behaviour. Participants then wore the LumoBack for a further five weeks whilst receiving feedback on sedentary behaviour via a sedentary vibration from the device and feedback on the mobile application. Sedentary behaviour, standing time, and stepping time were objectively assessed using the LumoBack. Differences in behaviour were determined between baseline, week 1 and week 5. Participant engagement with the LumoBack was determined using Mobile app analytics software. Results: There were no statistically significant differences in behaviour between baseline and the LumoBack intervention period (p>0.05). Participants engaged most with the Steps card on the LumoBack app with peaks in engagement seen at week 5. Conclusion: This study indicates that using the LumoBack on its own was not effective in reducing sedentary behaviour in adults. Self-monitoring and feedback may need to be combined with other behaviour change strategies such as environmental restructuring to be effective. General Conclusion This thesis found that there are currently an abundance of technologies which self-monitors physical activity but a lack of devices which measuring sedentary behaviour. One such device, the LumoBack, has shown to have acceptable validity as a measure of sedentary behaviour. Whilst the use of the LumoBack as a behaviour change tool did not elicit any significant changes, its ability to be a pervasive behavioural intervention and the use of user-defined nudging can make the LumoBack, and other similar low cost, valid objective sedentary behaviour self-monitors key components in multi-faceted interventions

Physical Activity Recognition and Identification System

Background: It is well-established that physical activity is beneficial to health. It is less known how the characteristics of physical activity impact health independently of total amount. This is due to the inability to measure these characteristics in an objective way that can be applied to large population groups. Accelerometry allows for objective monitoring of physical activity but is currently unable to identify type of physical activity accurately. Methods: This thesis details the creation of an activity classifier that can identify type from accelerometer data. The current research in activity classification was reviewed and methodological challenges were identified. The main challenge was the inability of classifiers to generalize to unseen data. Creating methods to mitigate this lack of generalisation represents the bulk of this thesis. Using the review, a classification pipeline was synthesised, representing the sequence of steps that all activity classifiers use. 1. Determination of device location and setting (Chapter 4) 2. Pre-processing (Chapter 5) 3. Segmenting into windows (Chapters 6) 4. Extracting features (Chapters 7,8) 5. Creating the classifier (Chapter 9) 6. Post-processing (Chapter 5) For each of these steps, methods were created and tested that allowed for a high level of generalisability without sacrificing overall performance. Results: The work in this thesis results in an activity classifier that had a good ability to generalize to unseen data. The classifier achieved an F1-score of 0.916 and 0.826 on data similar to its training data, which is statistically equivalent to the performance of current state of the art models (0.898, 0.765). On data dissimilar to its training data, the classifier achieved a significantly higher performance than current state of the art methods (0.759, 0.897 versus 0.352, 0.415). This shows that the classifier created in this work has a significantly greater ability to generalise to unseen data than current methods. Conclusion: This thesis details the creation of an activity classifier that allows for an improved ability to generalize to unseen data, thus allowing for identification of type from acceleration data. This should allow for more detailed investigation into the specific health effects of type in large population studies utilising accelerometers

Wi-Fi based people tracking in challenging environments

People tracking is a key building block in many applications such as abnormal activity detection, gesture recognition, and elderly persons monitoring. Video-based systems have many limitations making them ineffective in many situations. Wi-Fi provides an easily accessible source of opportunity for people tracking that does not have the limitations of video-based systems. The system will detect, localise, and track people, based on the available Wi-Fi signals that are reflected from their bodies. Wi-Fi based systems still need to address some challenges in order to be able to operate in challenging environments. Some of these challenges include the detection of the weak signal, the detection of abrupt people motion, and the presence of multipath propagation. In this thesis, these three main challenges will be addressed. Firstly, a weak signal detection method that uses the changes in the signals that are reflected from static objects, to improve the detection probability of weak signals that are reflected from the person’s body. Then, a deep learning based Wi-Fi localisation technique is proposed that significantly improves the runtime and the accuracy in comparison with existing techniques. After that, a quantum mechanics inspired tracking method is proposed to address the abrupt motion problem. The proposed method uses some interesting phenomena in the quantum world, where the person is allowed to exist at multiple positions simultaneously. The results show a significant improvement in reducing the tracking error and in reducing the tracking delay