Unsupervised IMU-based evaluation of at-home exercise programmes:a feasibility study

Background: The benefits to be obtained from home-based physical therapy programmes are dependent on the proper execution of physiotherapy exercises during unsupervised treatment. Wearable sensors and appropriate movement-related metrics may be used to determine at-home exercise performance and compliance to a physical therapy program. Methods: A total of thirty healthy volunteers (mean age of 31 years) had their movements captured using wearable inertial measurement units (IMUs), after video recordings of five different exercises with varying levels of complexity were demonstrated to them. Participants were then given wearable sensors to enable a second unsupervised data capture at home. Movement performance between the participants’ recordings was assessed with metrics of movement smoothness, intensity, consistency and control. Results: In general, subjects executed all exercises similarly when recording at home and as compared with their performance in the lab. However, participants executed all movements faster compared to the physiotherapist’s demonstrations, indicating the need of a wearable system with user feedback that will set the pace of movement. Conclusion: In light of the Covid-19 pandemic and the imperative transition towards remote consultation and tele-rehabilitation, this work aims to promote new tools and methods for the assessment of adherence to home-based physical therapy programmes. The studied IMU-derived features have shown adequate sensitivity to evaluate home-based programmes in an unsupervised manner. Cost-effective wearables, such as the one presented in this study, can support therapeutic exercises that ought to be performed with appropriate speed, intensity, smoothness and range of motion

Unsupervised IMU-based evaluation of at-home exercise programmes: a feasibility study

Background: The benefits to be obtained from home-based physical therapy programmes are dependent on the proper execution of physiotherapy exercises during unsupervised treatment. Wearable sensors and appropriate movement-related metrics may be used to determine at-home exercise performance and compliance to a physical therapy program. Methods: A total of thirty healthy volunteers (mean age of 31 years) had their movements captured using wearable inertial measurement units (IMUs), after video recordings of five different exercises with varying levels of complexity were demonstrated to them. Participants were then given wearable sensors to enable a second unsupervised data capture at home. Movement performance between the participants’ recordings was assessed with metrics of movement smoothness, intensity, consistency and control. Results: In general, subjects executed all exercises similarly when recording at home and as compared with their performance in the lab. However, participants executed all movements faster compared to the physiotherapist’s demonstrations, indicating the need of a wearable system with user feedback that will set the pace of movement. Conclusion: In light of the Covid-19 pandemic and the imperative transition towards remote consultation and tele-rehabilitation, this work aims to promote new tools and methods for the assessment of adherence to home-based physical therapy programmes. The studied IMU-derived features have shown adequate sensitivity to evaluate home-based programmes in an unsupervised manner. Cost-effective wearables, such as the one presented in this study, can support therapeutic exercises that ought to be performed with appropriate speed, intensity, smoothness and range of motion

Unsupervised IMU-based evaluation of at-home exercise programmes: A feasibility study

From Springer Nature via Jisc Publications RouterFunder: Science Foundation Ireland; doi: http://dx.doi.org/10.13039/501100001602; Grant(s): 12/RC/2289-P2Background: The benefits to be obtained from home-based physical therapy programmes are dependent on the proper execution of physiotherapy exercises during unsupervised treatment. Wearable sensors and appropriate movement-related metrics may be used to determine at-home exercise performance and compliance to a physical therapy program. Methods: A total of thirty healthy volunteers (mean age of 31 years) had their movements captured using wearable inertial measurement units (IMUs), after video recordings of five different exercises with varying levels of complexity were demonstrated to them. Participants were then given wearable sensors to enable a second unsupervised data capture at home. Movement performance between the participants’ recordings was assessed with metrics of movement smoothness, intensity, consistency and control. Results: In general, subjects executed all exercises similarly when recording at home and as compared with their performance in the lab. However, participants executed all movements faster compared to the physiotherapist’s demonstrations, indicating the need of a wearable system with user feedback that will set the pace of movement. Conclusion: In light of the Covid-19 pandemic and the imperative transition towards remote consultation and tele-rehabilitation, this work aims to promote new tools and methods for the assessment of adherence to home-based physical therapy programmes. The studied IMU-derived features have shown adequate sensitivity to evaluate home-based programmes in an unsupervised manner. Cost-effective wearables, such as the one presented in this study, can support therapeutic exercises that ought to be performed with appropriate speed, intensity, smoothness and range of motion.This work was supported in part by the Science Foundation Ireland (SFI) under Grant numbers 12/RC/2289-P2 (INSIGHT), 13/RC/2077 (CONNECT) and 16/RC/3918 (CONFIRM) which are co-funded under the European Regional Development Fund (ERDF).14pubpu

CURRENT TOPICS IN LOCOMOTION PHYSIOLOGY: A) MUSCLE EFFICIENCY IN HEAVILY LOADED GRADIENT WALKING AND B) HEART RATE OFF-KINETICS AS A PREDICTOR OF VO2MAX

The human locomotion has been constantly analysed from both bioenergetics and biomechanical point of views (Saibene & Minetti, 2003; Cavagna, 2010). Since earliest times, hunting for food and escaping from predators already has proven how important is to comprehend this complex engineering that is our locomotor machine. Gradient locomotion has been investigated in the past, and the concept of the optimum gradient for walking, running and mountain paths are well known in the literature. The existence of an optimum gradient is based on the different partitioning between positive and negative mechanical work (that mirrors concentric and eccentric muscular activity) and the related metabolic demand. In the literature, the ratio between negative and positive work efficiency during unloaded locomotion was found to be 5/1. The purpose of this new study is to analyse the mechanical, metabolic and electromyography parameters during gradient loaded walking in order to understand how an extra-load can affect locomotion and especially the efficiency of positive and negative work. Still, another important topic referred to the human locomotion physiology is about the cardiovascular system. Related to this, oxygen uptake (V'O2) refers to the product of cardiac output and the volume of oxygen extracted from the blood, and its maximal value (V'O2peak) strengthen the maximal capacity of the cardiovascular system to provide O2 to muscle cells during continued exercise, being the most widely used measure of physical fitness (Plasqui & Westerterp, 2005; Koeneman et al., 2011). Although there is a large genetic component, it is mainly determined by a person\u2019s activity level, and inversely related to several health outcomes such as cardiovascular disease (Daanen et al., 2012). Besides of heart rate (HR) control and its relationship with V'O2, the HR recovery (off transient, after exercise) has received more attention by current researchers (Myers et al., 2007; Dupuy et al., 2012; Haddad et al., 2012). The rate of decline in HR following termination of exercise, which is regulated by the autonomic nervous system and thereby, provides information concerning sympathetic and parasympathetic activity (Daanen et al., 2012). In general, the more rapid the HR recovery, the better the fitness (Daanen et al., 2012; Buchheit, 2014). While exercise-training studies usually report HR values at a given time during the recovery period (Daanen et al., 2012), in most clinical studies, HR recovery is defined as the difference between HR at the end of exercise and HR at a given time during the recovery period (Otsuki et al., 2007; Dupuy et al., 2012). Moreover, in some studies a mono-exponential model fit the HR off-kinetics to derive global parameters of HR recovery kinetics such as the time constant or the asymptotic value (Stanley et al., 2013; Peinado et al., 2014). Based on these results and the growing interest in new smart devices for health monitoring, here we aimed to estimate V'O2peak from a short test (60 m) with variables that can be detected by the smart sensors. We ask to 25 healthy subjects to perform a maximal sprint over 60-m. Beat-by-beat HR was recorded by a chest belt during the whole test including resting period before and recovery post sprint. (n = 25). HR off kinetics was fitted by a mono-exponential function and tau value was calculated in order to obtain a velocity of HR decrement post exercise. V'O2peak was then estimate with a multiple regression analysis: V'O2peak = 7.46 19vtest + 261.4 19voff - 0.19 19 06HR (R2= 0.65, p<0.001). Where vtest represents the velocity performed during the 60-m test, voff corresponds to the velocity of HR decreasing during off-transient (recovery phase), and " 06HR" is the difference of HR during on-transient of exercise and it is the difference between maximum and the resting value. This new equation does not aim to replace the laboratory-standard protocols for V'O2peak determination, but it can give an insight about fitness level to laymen that use smart devices for monitoring their physical activity. Whenever these new models (smart watches) would perform a beat-by-beat analysis this equation could be introduced to the software and give a first general estimate of the user's fitness level

Wearable Energy Harvesting for Charging Portable Electronic Devices by Walking

Wearable energy harvesting technologies will become an everyday part of future portable electronic devices. By generating the energy where the energy is needed and not relying on a main power source to recharge the portable devices battery, wearable energy harvesters will enable future generations to have even more freedoms, travel further, and never run low on battery again. This will reduce the energy consumption of the mains grid and thus in turn reduce CO² emissions generated by this traditional power source making this research important for the whole plant. This research project aims to take another step towards in helping the development of future technologies by investigating novel wearable energy harvesting designs and showing ability to charge current portable electronic devices such as smart phones and tables. This required research into a broad range of topics including, energies from humans, energy conversion mechanisms, the movement of people and the power demands for charging current portable electronic devices. Background research in the human energy levels and how research to date had gone about exacting different energy sources in different ways was the starting point for this research. This leads on to a more detailed look into the exaction methods and optimization of footfall energy harvester designs. Looking into the human gait cycle gave the information required to replicate human footfall motion for use in scientific experiments. From this background research, two bespoke designs of wearable energy harvester have been created. The first novel design showed a promising way of extracting footfall energy and converting it into useable electrical energy producing Watt-Level of power. The second design is an evolution of the first design but expands the extraction method to both feet and relocated the main harvester unit into a backpack worn by the user. The improved design incorporates a novel approach to energy conversion method by introducing a mechanical energy storage system before transduction into electrical energy. This is shown to increased electrical power output from footfall energy, reduced energy consumption of the wearer and is shown to truly be able to charge current portable electronics. The improved design is shown to produce 2.6 Watts average power from normal walking. The experimental set ups, procedures, and their results are shown throughout this thesis. These experimental results are confirmed by using the wearable energy harvesters on a treadmill at the three main walking speeds showing their real-world capabilities. To demonstrate the wearable energy harvester deigns shown in this research project were truly able to charge current portable technologies, endurance testing was also performed. This confirms the harvesters were able to work for longer periods of time. This longer time frame is needed for the charging times of the current portable devices. After researching into wearable energy harvesting from over the last 20 years it was a struggle to compare all the different forms, designs, types and power outputs. It became clear that the existing methods were unable to provide a clear picture of harvester’s scalability, changeability and useability for future design ideas. This is why a new form of comparison was created and is shown to have strong benefits over the existing methods