Combining accelerometry with allometry for estimating daily energy expenditure in joules when in-lab calibration is unavailable.

BACKGROUND: All behaviour requires energy, and measuring energy expenditure in standard units (joules) is key to linking behaviour to ecological processes. Animal-borne accelerometers are commonly used to infer proxies of energy expenditure, termed 'dynamic body acceleration' (DBA). However, converting acceleration proxies (m/s2) to standard units (watts) involves costly in-lab respirometry measurements, and there is a lack of viable substitutes for empirical calibration relationships when these are unavailable. METHODS: We used past allometric work quantifying energy expenditure during resting and locomotion as a function of body mass to calibrate DBA. We used the resulting 'power calibration equation' to estimate daily energy expenditure (DEE) using two models: (1) locomotion data-based linear calibration applied to the waking period, and Kleiber's law applied to the sleeping period (ACTIWAKE), and (2) locomotion and resting data-based linear calibration applied to the 24-h period (ACTIREST24). Since both models require locomotion speed information, we developed an algorithm to estimate speed from accelerometer, gyroscope, and behavioural annotation data. We applied these methods to estimate DEE in free-ranging meerkats (Suricata suricatta), and compared model estimates with published DEE measurements made using doubly labelled water (DLW) on the same meerkat population. RESULTS: ACTIWAKE's DEE estimates did not differ significantly from DLW (t(19) = - 1.25; P = 0.22), while ACTIREST24's estimates did (t(19) = - 2.38; P = 0.028). Both models underestimated DEE compared to DLW: ACTIWAKE by 14% and ACTIREST by 26%. The inter-individual spread in model estimates of DEE (s.d. 1-2% of mean) was lower than that in DLW (s.d. 33% of mean). CONCLUSIONS: We found that linear locomotion-based calibration applied to the waking period, and a 'flat' resting metabolic rate applied to the sleeping period can provide realistic joule estimates of DEE in terrestrial mammals. The underestimation and lower spread in model estimates compared to DLW likely arise because the accelerometer only captures movement-related energy expenditure, whereas DLW is an integrated measure. Our study offers new tools to incorporate body mass (through allometry), and changes in behavioural time budgets and intra-behaviour changes in intensity (through DBA) in acceleration-based field assessments of daily energy expenditure.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions.

Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

In-field assessment of change-of-direction ability with a single wearable sensor.

The Agility T-test is a standardized method to measure the change-of-direction (COD) ability of athletes in the field. It is traditionally scored based on the total completion time, which does not provide information on the different CODs. Augmenting the T-test with wearable sensors provides the opportunity to explore new metrics. Towards this, data of 23 professional soccer players were recorded with a trunk-worn GNSS-IMU (Global Navigation Satellite System-Inertial Measurement Unit) device. A method for detecting the four CODs based on the wavelet-denoised antero-posterior acceleration signal was developed and validated using video data (60 Hz). Following this, completion time was estimated using GNSS ground speed and validated with the photocell data. The proposed method yields an error (mean ± standard deviation) of 0 ± 66 ms for the COD detection, - 0.16 ± 0.22 s for completion time, and a relative error for each COD duration and each sequential movement durations of less than 3.5 ± 16% and 7 ± 7%, respectively. The presented algorithm can highlight the asymmetric performance between the phases and CODs in the right and left direction. By providing a more comprehensive analysis in the field, this work can enable coaches to develop more personalized training and rehabilitation programs.

Impact of adherence to a lifestyle-integrated programme on physical function and behavioural complexity in young older adults at risk of functional decline: a multicentre RCT secondary analysis.

CONTEXT: Long-term adherence to physical activity (PA) interventions is challenging. The Lifestyle-integrated Functional Exercise programmes were adapted Lifestyle-integrated Functional Exercise (aLiFE) to include more challenging activities and a behavioural change framework, and then enhanced Lifestyle-integrated Functional Exercise (eLiFE) to be delivered using smartphones and smartwatches. OBJECTIVES: To (1) compare adherence measures, (2) identify determinants of adherence and (3) assess the impact on outcome measures of a lifestyle-integrated programme. DESIGN, SETTING AND PARTICIPANTS: A multicentre, feasibility randomised controlled trial including participants aged 61-70 years conducted in three European cities. INTERVENTIONS: Six-month trainer-supported aLiFE or eLiFE compared with a control group, which received written PA advice. OUTCOME MEASURES: Self-reporting adherence per month using a single question and after 6-month intervention using the Exercise Adherence Rating Scale (EARS, score range 6-24). Treatment outcomes included function and disability scores (measured using the Late-Life Function and Disability Index) and sensor-derived physical behaviour complexity measure. Determinants of adherence (EARS score) were identified using linear multivariate analysis. Linear regression estimated the association of adherence on treatment outcome. RESULTS: We included 120 participants randomised to the intervention groups (aLiFE/eLiFE) (66.3±2.3 years, 53% women). The 106 participants reassessed after 6 months had a mean EARS score of 16.0±5.1. Better adherence was associated with lower number of medications taken, lower depression and lower risk of functional decline. We estimated adherence to significantly increase basic lower extremity function by 1.3 points (p<0.0001), advanced lower extremity function by 1.0 point (p<0.0001) and behavioural complexity by 0.008 per 1.0 point higher EARS score (F(3,91)=3.55, p=0.017) regardless of group allocation. CONCLUSION: PA adherence was associated with better lower extremity function and physical behavioural complexity. Barriers to adherence should be addressed preintervention to enhance intervention efficacy. Further research is needed to unravel the impact of behaviour change techniques embedded into technology-delivered activity interventions on adherence. TRIAL REGISTRATION NUMBER: NCT03065088.

Level, Uphill, and Downhill Running Economy Values Are Correlated Except on Steep Slopes.

The aim of this study was first to determine if level, uphill, and downhill energy cost of running (ECR) values were correlated at different slopes and for different running speeds, and second, to determine the influence of lower limb strength on ECR. Twenty-nine healthy subjects completed a randomized series of 4-min running bouts on an instrumented treadmill to determine their cardiorespiratory and mechanical (i.e., ground reaction forces) responses at different constant speeds (8, 10, 12, and 14 km·h-1) and different slopes (-20, -10, -5, 0, +5, +10, +15, and +20%). The subjects also performed a knee extensor (KE) strength assessment. Oxygen and energy costs of running values were correlated between all slopes by pooling all running speeds (all r 2 ≥ 0.27; p ≤ 0.021), except between the steepest uphill vs. level and the steepest downhill slope (i.e., +20% vs. 0% and -20% slopes; both p ≥ 0.214). When pooled across all running speeds, the ECR was inversely correlated with KE isometric maximal torque for the level and downhill running conditions (all r 2 ≥ 0.24; p ≤ 0.049) except for the steepest downhill slope (-20%), but not for any uphill slopes. The optimal downhill grade (i.e., lowest oxygen cost) varied between running speeds and ranged from -14% and -20% (all p < 0.001). The present results suggest that compared to level and shallow slopes, on steep slopes ~±20%, running energetics are determined by different factors (i.e., reduced bouncing mechanism, greater muscle strength for negative slopes, and cardiopulmonary fitness for positive slopes). On shallow negative slopes and during level running, ECR is related to KE strength.

Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease.

Accurate assessment of Parkinson's disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD.

Toward a Remote Assessment of Walking Bout and Speed: Application in Patients With Multiple Sclerosis.

Gait speed as a powerful biomarker of mobility is mostly assessed in controlled environments, e.g. in the clinic. With wearable inertial sensors, gait speed can be estimated in an objective manner. However, most of the previous works have validated the gait speed estimation algorithms in clinical settings which can be different than the home assessments in which the patients demonstrate their actual performance. Moreover, to provide comfort for the users, devising an algorithm based on a single sensor setup is essential. To this end, the goal of this study was to develop and validate a new gait speed estimation method based on a machine learning approach to predict gait speed in both clinical and home assessments by a sensor on the lower back. Moreover, two methods were introduced to detect walking bouts during daily activities at home. We have validated the algorithms in 35 patients with multiple sclerosis as it often presents with mobility difficulties. Therefore, the robustness of the algorithm can be shown in an impaired or slow gait. Against silver standard multi-sensor references, we achieved a bias close to zero and a precision of 0.15 m/s for gait speed estimation. Furthermore, the proposed machine learning-based locomotion detection method had a median of 96.8% specificity, 93.0% sensitivity, 96.4% accuracy, and 78.6% F1-score in detecting walking bouts at home. The high performance of the proposed algorithm showed the feasibility of the unsupervised mobility assessment introduced in this study.

Long-term unsupervised mobility assessment in movement disorders.

Mobile health technologies (wearable, portable, body-fixed sensors, or domestic-integrated devices) that quantify mobility in unsupervised, daily living environments are emerging as complementary clinical assessments. Data collected in these ecologically valid, patient-relevant settings can overcome limitations of conventional clinical assessments, as they capture fluctuating and rare events. These data could support clinical decision making and could also serve as outcomes in clinical trials. However, studies that directly compared assessments made in unsupervised and supervised (eg, in the laboratory or hospital) settings point to large disparities, even in the same parameters of mobility. These differences appear to be affected by psychological, physiological, cognitive, environmental, and technical factors, and by the types of mobilities and diagnoses assessed. To facilitate the successful adaptation of the unsupervised assessment of mobility into clinical practice and clinical trials, clinicians and researchers should consider these disparities and the multiple factors that contribute to them.

Gait Symmetry Assessment with a Low Back 3D Accelerometer in Post-Stroke Patients.

Gait asymmetry is an important marker of mobility impairment post stroke. This study proposes a new gait symmetry index (GSI) to quantify gait symmetry with one 3D accelerometer at L3 (GSIL3). GSIL3 was evaluated with 16 post stroke patients and nine healthy controls in the Six-Minute-Walk-Test (6-MWT). Discriminative power was evaluated with Wilcoxon test and the effect size (ES) was computed with Cliff's Delta. GSIL3 estimated during the entire 6-MWT and during a short segment straight walk (GSIL3straight) have comparable effect size to one another (ES = 0.89, p < 0.001) and to the symmetry indices derived from feet sensors (|ES| = [0.22, 0.89]). Furthermore, while none of the indices derived from feet sensors showed significant differences between post stroke patients walking with a cane compared to those able to walk without, GSIL3 was able to discriminate between these two groups with a significantly lower value in the group using a cane (ES = 0.70, p = 0.02). In addition, GSIL3 was strongly associated with several symmetry indices measured by feet sensors during the straight walking cycles (Spearman correlation: |ρ| = [0.82, 0.88], p < 0.05). The proposed index can be a reliable and cost-efficient post stroke gait symmetry assessment with implications for research and clinical practice.

A New Training Assessment Method for Alpine Ski Racing: Estimating Center of Mass Trajectory by Fusing Inertial Sensors With Periodically Available Position Anchor Points.

In this study we present and validate a method to correct velocity and position drift for inertial sensor-based measurements in the context of alpine ski racing. Magnets were placed at each gate and their position determined using a land surveying method. The time point of gate crossings of the athlete were detected with a magnetometer attached to the athlete's lower back. A full body inertial sensor setup allowed to track the athlete's posture, and the magnet positions were used as anchor points to correct position and velocity drift from the integration of the acceleration. Center of mass (CoM) position errors (mean ± standard deviation) were 0.24 m ± 0.09 m and CoM velocity errors were 0.00 m/s ± 0.18 m/s. For extracted turn entrance and exit speeds the 95% limits of agreements (LoAs) were between -0.19 and 0.33 m/s. LoA for the total path length of a turn were between -0.06 and 0.16 m. The proposed setup and processing allowed estimating the CoM kinematics with similar errors than known for differential global navigation satellite systems (GNSS), even though the athlete's movement was measured with inertial and magnetic sensors only. Moreover, as the gate positions can also be obtained with non-GNSS based land surveying methods, CoM kinematics may be estimated in areas with reduced or no GNSS signal reception, such as in forests or indoors.

How accurate is visual determination of foot strike pattern and pronation assessment.

Nowadays, choosing adequate running shoes is very difficult, due to the high number of different designs. Nevertheless, shoes have two main characteristics to fit runners' technique and morphology: drop and arch support. Retailers' advices are usually based on the visual assessment of the customer's running technique. Such method is subjective and requires an experimented examiner while objective methods require expensive material, such as 3D motion system and pressure insoles. Therefore, the aim of this study was to determine the accuracy of foot strike pattern and pronation assessment using video cameras, compared to a gold standard motion tracking system and pressure insoles. 34 subjects had to run at 8, 12 and 16 Km/h shod and 12 Km/h barefoot during 30 s trials on a treadmill. Agreement between foot strike pattern assessment methods was between 88% and 92%. For pronation, agreement on assessment methods was between 42% and 56%. The results obtained indicate a good accuracy on foot strike pattern assessment, and a high difficulty to determine pronation with enough accuracy. There is therefore a need to develop new tools for the assessment of runner's pronation.

Assessment of the lower limb soft tissue artefact at marker-cluster level with a high-density marker set during walking.

The estimation of joint kinematics from skin markers is hindered by the soft tissue artefact (STA), a well-known phenomenon although not fully characterized. While most assessments of the STA have been performed based on the individual skin markers displacements, recent assessments were based on the marker-cluster geometrical transformations using, e.g., principal component or modal analysis. However, these marker-clusters were generally made of 4-6 markers and the current findings on the STA could have been biased by the limited number of skin makers analysed. The objective of the present study was therefore to confirm them with a high-density marker set, i.e. 40 markers placed on the segments. A larger number of modes than found in the literature was required to describe the STA. Nevertheless, translations and rotations of the marker-cluster remained the main STA modes, archetypally the translation along the proximal-distal and anterior-posterior axes for the shank and the translation along the proximal-distal axis and the rotation about the medial-lateral axis for the thigh. High correlations were also found between the knee flexion angle and the amplitude of these modes for the thigh whereas moderate ones were found for the shank. These findings support the current re-orientation of the STA compensation methods, from bone pose estimators which typically address the non-rigid components of the marker-cluster to kinematic-driven rigid-component STA models.

Assessment of physical activity of patients with chronic pain.

OBJECTIVES: To define the key terms and concepts relating physical activity to chronic pain; to provide a brief overview of the various methods of assessment of physical activity; to review the current literature about physical activity and chronic pain; and to identify needs for future research. MATERIALS AND METHODS: A narrative review based on results of a PubMed search (to May 2011) and the references of recent systematic reviews. RESULTS: Many methods exist for measuring physical activity. Movement sensors, such as accelerometers, offer objective assessment of physical activity of patients with chronic pain. It is often assumed that patients who feel disabled and report daily life restrictions due to chronic pain also will be less physically active. Studies that have compared the activity of patients with chronic back pain with that of healthy individuals consistently showed that the relationship of physical activity and severity of pain, as well as the change in activity following interventions, was variable and complex. CONCLUSIONS: It is important to understand the relationship between physical activity and chronic pain. Future studies should objectively assess not only the pattern and complexity of that relationship but also the interaction with the patient's mood and ability to cope with the pain.

Soft tissue artifact assessment during treadmill walking in subjects with total knee arthroplasty.

Accurate measurement of knee kinematics during functional activities suffers mainly from soft tissue artifact (STA): the combination of local surface deformations and rigid movement of markers relative to the underlying bone (also called rigid STA movement: RSTAM). This study proposes to assess RSTAM on the thigh, shank, and knee joint and to observe possible features between subjects. Nineteen subjects with knee arthroplasty were asked to walk on a treadmill while a biplane fluoroscopic system (X-rays) and a stereophotogrammetric system (skin markers) recorded their knee movement. The RSTAM was defined as the rigid movement of the cluster of skin markers relative to the prosthesis. The results showed that RSTAM amplitude represents approximately 80-100% of the STA. The vertical axis of the anatomical frame of the femur was influenced the most by RSTAM. Combined with tibial error, internal/external rotation angle and distraction-compression were the knee kinematics parameters most affected by RSTAM during the gait cycle, with average rms values of 3.8° and 11.1 mm. This study highlighted higher RSTAM during the swing phase particularly in the thigh segment and suggests new features for RSTAM such as the particular shape of some RSTAM waveforms and the absence of RSTAM in certain kinematics during the gait phases. The comparison of coefficient of multiple correlations showed some similarities of RSTAM between subjects, while some correlations were found with gait speed and BMI. These new insights could potentially allow the development of new methods of compensation to avoid STA.

On-shoe wearable sensors for gait and turning assessment of patients with Parkinson's disease.

Assessment of locomotion through simple tests such as timed up and go (TUG) or walking trials can provide valuable information for the evaluation of treatment and the early diagnosis of people with Parkinson's disease (PD). Common methods used in clinics are either based on complex motion laboratory settings or simple timing outcomes using stop watches. The goal of this paper is to present an innovative technology based on wearable sensors on-shoe and processing algorithm, which provides outcome measures characterizing PD motor symptoms during TUG and gait tests. Our results on ten PD patients and ten age-matched elderly subjects indicate an accuracy ± precision of 2.8 ± 2.4 cm/s and 1.3 ± 3.0 cm for stride velocity and stride length estimation compared to optical motion capture, with the advantage of being practical to use in home or clinics without any discomfort for the subject. In addition, the use of novel spatio-temporal parameters, including turning, swing width, path length, and their intercycle variability, was also validated and showed interesting tendencies for discriminating patients in ON and OFF states and control subjects.

Gait assessment in children with duchenne muscular dystrophy during long-distance walking.

The aim of this study was to investigate the alteration of the gait pattern in 25 children with Duchenne muscular dystrophy, using body-worn inertial sensors during a long walking distance. Normalized spatiotemporal gait parameters and their variability were extracted from the angular velocity of the shanks; the smoothness of the trunk movement was assessed based on the spectral entropy of the acceleration norm. As compared to healthy children, patients with Duchenne muscular dystrophy showed significantly lower stride velocity and a less smooth trunk movement. When the group of patients was divided into mild and moderate based on the Motor Function Measure, the authors noticed significantly higher values both for cadence and stride velocity, as well as improved trunk smoothness in the mild versus moderate group. The potential of such parameters to distinguish between different disease states opens new perspectives for the objective assessment of efficacy of the new therapies associated with Duchenne muscular dystrophy.

Assessment of physical activity in older people with and without cognitive impairment.

The purpose of this study was to validate a new interview-administered physical activity questionnaire (Assessment of Physical Activity in Frail Older People; APAFOP) in older people with and without cognitive impairment. The authors assessed feasibility, validity, and test-retest reliability in 168 people (n = 78 with, n = 88 without cognitive impairment). Concurrent validity was assessed against an inertia-based motion sensor and an established questionnaire. Sensitivity to change was tested in an ongoing study in patients with mild to moderate dementia (n = 81). Assessment of physical activity by the APAFOP and the motion sensor correlated well in the total sample (TS; p = .705), as well as in the subsamples with cognitive impairment (CI; p = .585) and without CI (p = .787). Excellent feasibility with an acceptance rate of 100%, test-retest reliability (intraclass correlation coefficients ranging from .973 (TS) to .975 (CI) to .966 (no CI), and sensitivity to change (effect sizes: 0.35-1.47) were found in both subsamples.