Validation of the SenseWear Pro 2 Armband Calorimeter to Assess Energy Expenditure of Adolescents during Various Modes of Activity

VALIDATION OF THE SENSEWEAR PRO2 ARMBAND CALORIMETER TO ASSESS ENERGY EXPENDITURE OF ADOLESCENTS DURING VARIOUS MODES OF ACTIVITYKim Crawford, PhDUniversity of Pittsburgh, 2004The primary purpose of this investigation was to examine the validity of the SenseWear® Pro 2 Armband (SAB) to assess energy expenditure during various modes of physical activity in adolescents. It was hypothesized that measures of energy expenditure during treadmill and cycle ergometer exercise would not differ between the SAB and the criterion respiratory metabolic system (RMS) when examined for female and male subjects. Twenty-four healthy adolescents completed both the cycle ergometer and treadmill exercise protocols. The primary findings of this investigation were the SAB significantly underestimated energy expenditure during cycle ergometer exercise at the low (1.53 + 0.60 kcal.min-1; P<0.001) and moderate (2.48 + 0.95 kcal.min-1; P<0.001) intensities and for total energy expenditure (19.11 + 7.43 kcal; P<0.001) in both the female and male subjects. In the treadmill exercise, there were no significant differences between measures of energy expenditure during treadmill walking at 3.0 mph, 0% incline in female and male subjects. However, the SAB significantly underestimated measures of energy expenditure at 4.0 mph, 0% grade (0.86 + 0.84 kcal.min-1; P<0.001); 4.0 mph, 5% grade (2.13 + 1.40 kcal.min-1; P<0.001); 4.5 mph, 5% grade (2.97 + 1.56 kcal.min-1; P<0.001) and for total energy expenditure (23.66 + 14.92 kcal; P<0.001) during treadmill exercise in female and male subjects.Possible mechanisms underlying the underestimation of energy expenditure by the SAB are complex but may include: the use of generalized exercise algorithms to predict all types of physical activity; possible disproportionate reliance on the two-axis accelerometer during non-weight bearing and graded exercises; the delay in body heat transfer to the skin; and the inability to account for variability in walking gait, lean body mass and fat mass. All of these factors impact on the accuracy of the SAB to accurately estimate energy expenditure. This is the first study to examine the accuracy of the SAB in adolescent subjects and is an important first step in validating SAB technology in adolescents

Principles of energy optimization underlying human walking gait adaptations

Learning to move in novel situations is a complex process. We need to continually learn the changing situations and determine the best way to move. Optimization is a widely accepted framework for this process. However, little is known about algorithms used by the nervous system to perform this optimization. Our lab recently found evidence that people can continuously optimize energy during walking. My goal in this thesis is to identify principles of optimization, particularly energy optimization in walking, that govern our choice of movement in novel situations. I used two novel walking tasks for this purpose. For the first task, I designed, built, and tested a mechatronic system that can quickly, accurately, and precisely apply forces to a user’s torso. It changes the relationship between a walking gait and its associated energetic cost—cost landscape—to shift the energy optimal walking gait. Participants shift their gait towards the new optimum in these landscapes. In my second project, I aimed to understand how the nervous system identifies when to initiate optimization. I used my system to create cost landscapes of three different cost gradients. I found that experiencing a steeper cost gradient through natural variability is not sufficient to cue the nervous system to initiate optimization. For my third and fourth projects, I used the task of split-belt walking. I collaborated with another research group to analyse the mechanics and energetics of walking with different step lengths on a split-belt treadmill. I found that people can harness energy from a split-belt treadmill by placing their leading leg further forward on the fast belt, and that there may be an energy optimal gait. In my fourth project, I used computer modelling to identify that there may exist an energy optimal gait due to the trade-off between the cost of swinging the leg and the cost of redirecting the body center of mass when transitioning from step to step. Together, these projects develop a new system and a new approach to understand energy optimization in walking. They uncover principles governing the initiation of this process and our ability to benefit from it

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Comparison Marker-Based and Markerless Motion Capture Systems in Gait Biomechanics During Running

Background: Markerless (ML) motion capture systems have recently become available for biomechanics applications. Evidence has indicated the potential feasibility of using an ML system to analyze lower extremity kinematics. However, no research examined ML systems’ estimation of the lower extremity joint moments and powers. Objectives: This study primarily aimed to compare lower extremity joint moments and powers estimated by marker-based (MB) and ML motion capture systems during treadmill running. The secondary purpose was to investigate if movement’s speed would affect the ML’s performance. Methods: Sixteen volunteers ran on a treadmill for 120 s for each trial at the speed of 2.24, 2.91, and 3.58 m/s, respectively. The kinematic data were simultaneously recorded by 8 infrared cameras and 8 high-resolution video cameras. The force data were recorded via an instrumented treadmill. Results: Compared to the MB system, the ML system estimated greater increased hip and knee joint kinetics with faster speeds during the swing phase. Additionally, increased greater ankle joint moments with speed estimated by the ML system were observed at the early swing phase. In contrast, the greater ankle joint powers occurred at the initial stance phase. Conclusions: These observations indicated that inconsistent segment pose estimations (mainly the center of mass estimated by ML was farther away from the relevant distal joint center) might lead to systematic differences in joint moments and powers estimated by MB and ML systems. Despite the promising applications of the ML system in clinical settings, systematic ML overestimation requires extra attention

Feasibility and efficacy of incorporating an exoskeleton in gait training during subacute stroke rehabilitation

Introduction: Hemiparesis is the most common acute manifestation of stroke and often has a strong negative impact on walking ability leaving one third of patients dependent in walking activities outside one’s home. Improved methods for training of gait during stroke rehabilitation could tackle the challenge of achieving independent walking and promote better outcomes. Several studies have explored the value of introducing electromechanical gait machines in stroke rehabilitation to enhance gait training. One example is the exoskeleton Hybrid Assistive Limb (HAL). The HAL system has been found feasible to use during rehabilitation in the chronic stage after stroke, however knowledge of the feasibility in the subacute stage after stroke and its efficacy compared to evidence-based conventional gait training is still limited. Aim: The overall aim of this thesis was to evaluate the safety and feasibility of HAL for gait training in the subacute stage after stroke and the effect of HAL training on functioning, disability and health compared to conventional gait training, as part of an inpatient rehabilitation program in patients with severe limitations in walking in the subacute stage after stroke. Methods: This thesis contains two studies where one is a safety and feasibility study (Study I) and one is a prospective, randomized, open labeled, blinded evaluation study (Study II). In Study I, eight patients performed HAL training 5 days/week. The number of training sessions were adjusted individually and varied from 6 to 31 (median 16). Safety and feasibility aspects of the training were evaluated as well as clinical outcomes on functioning and disability (e.g. independence in walking, walking speed, balance, movement functions and activities of daily living), assessed before and after the intervention period. In Study II, 32 patients were randomized to either conventional training only or HAL training in addition to the conventional training, 4 days per week for 4 weeks. Within and between- group differences in independence in walking, walking speed/endurance, balance, movement functions and activities of daily living were investigated before and after the intervention period, as well as 6 months post stroke. In addition, gait pattern functions were evaluated after the intervention in a three-dimensional gait laboratory. At 6 months post stroke self- perceived aspects on functioning disability and health were assessed and subsequently correlated to the clinical assessments. Results: In Study I HAL was found to be safe and feasible for gait training after stroke in patients with hemiparesis, unable to walk independently, undergoing an inpatient rehabilitation program. All patients improved in walking independence and speed, movement function, and activities of daily living during the intervention period. In addition, it was found that patients walked long distances during the HAL sessions, suggesting that HAL training may be an effective method to enhance gait training during rehabilitation of patients in the subacute stage after stroke. In Study II substantial but equal improvements in the clinically evaluated outcomes in the two intervention groups were found. At six months post stroke, two thirds of patients were independent in walking, and a younger age but not intervention group served as the best predictor. Gait patterns were similarly impaired in both groups and in line with previous reports on gait patterns post stroke. Further, self-perceived ratings on functioning, disability and health were explained by the ability to perform self-care activities and not by intervention group. Conclusion: To incorporate gait training with HAL is safe and feasible during inpatient rehabilitation in the subacute stage after stroke and may be a way to increase the dose (i.e. number of steps) in gait training in the subacute stage after stroke. Among these included younger patients with hemiparesis and severe limitations in walking in the subacute stage after stroke, substantial improvements in body function and activity as well as equally impaired gait patterns were observed both after incorporated HAL training and after conventional gait training only, but without between-group differences. In future studies, potential beneficial effects on cardiovascular, respiratory, and metabolic functions should be addressed. Further, as the stroke population is heterogeneous, potential subgroups of patients who may benefit the most from electromechanically-assisted gait training should be identified

Deep learning-based energy expenditure estimation in assisted and non-assisted gait using inertial, EMG, and heart rate wearable sensors

Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ > 0.85) between target and estimation (R¯2 = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control.This work has been supported in part by the FEDER Funds through the COMPETE 2020-Programa Operacional Competitividade e Internacionalizacao (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020, under the FCT scholarship with reference 2020.05708.BD, and under the Stimulus of Scientific Employment with the grant 2020.03393.CEECIND

A 'one-size-fits-most' walking recognition method for smartphones, smartwatches, and wearable accelerometers

The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using 'activity counts,' a measure which overlooks specific types of physical activities. We proposed a walking recognition method for sub-second tri-axial accelerometer data, in which activity classification is based on the inherent features of walking: intensity, periodicity, and duration. We validated our method against 20 publicly available, annotated datasets on walking activity data collected at various body locations (thigh, waist, chest, arm, wrist). We demonstrated that our method can estimate walking periods with high sensitivity and specificity: average sensitivity ranged between 0.92 and 0.97 across various body locations, and average specificity for common daily activities was typically above 0.95. We also assessed the method's algorithmic fairness to demographic and anthropometric variables and measurement contexts (body location, environment). Finally, we have released our method as open-source software in MATLAB and Python.Comment: 39 pages, 4 figures (incl. 1 supplementary), and 5 tables (incl. 2 supplementary