Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities

Functional assessment and satisfaction of transfemoral amputees with low mobility (FASTK2): A clinical trial of microprocessor-controlled vs. non-microprocessor-controlled knees

BACKGROUND

The benefits of a microprocessor-controlled knee are well documented in transfemoral amputees who are unlimited community ambulators. There have been suggestions that transfemoral amputees with limited community ambulation will also benefit from a microprocessor-controlled knee. Current medical policy restricts microprocessor-controlled knees to unlimited community ambulators and, thereby, potentially limits function. This clinical trial was performed to determine if limited community ambulators would benefit from a microprocessor-controlled knee.

METHODS

50 unilateral transfemoral amputees, mean age 69, were tested using their current non-microprocessor-controlled knee, fit with a microprocessor-controlled knee and allowed 10 weeks of acclimation before being tested, and then retested with their original mechanical knee after 4 weeks of re-acclimation. Patient function was assessed in the free-living environment using tri-axial accelerometers. Patient satisfaction and safety were also measured.

FINDINGS

The subjects demonstrated improved outcomes when using the microprocessor-controlled knee. Subjects had a significant reduction in falls, spent less time sitting, and increased their activity level. Subjects also reported significantly better ambulation, improved appearance, and greater utility.

INTERPRETATION

This clinical trial demonstrated that transfemoral amputees with limited mobility clearly benefit from a microprocessor-controlled knee. Notably, a reduction in falls occurred while the subjects engaged in more physical activity, which resulted in increased subject satisfaction. The increased activity resulted in a greater exposure to fall risk, but that risk was moderated by the advanced technology. ClinicalTrials.gov No: NCT02240186.

Step count accuracy of StepWatch and FitBit One™ among individuals with a unilateral transtibial amputation

BACKGROUND

Step counts, obtained via activity monitors, provide insight into activity level in the free-living environment. Accuracy assessments of activity monitors are limited among individuals with lower-limb amputations.

OBJECTIVES

(1) To evaluate the step count accuracy of both monitors during forward-linear and complex walking and (2) compare monitor step counts in the free-living environment.

STUDY DESIGN

Cross-sectional study.

METHODS

Adult prosthetic users with a unilateral transtibial amputation were equipped with StepWatch and FitBit One™. Participants completed an in-clinic evaluation to evaluate each monitor's step count accuracy during forward linear and complex walking followed by a 7-day step count evaluation in the free-living environment.

RESULTS

Both monitors showed excellent accuracy during forward, linear walking (intraclass correlation coefficients = 0.97-0.99, 95% confidence interval = 0.93-0.99; percentage error = 4.3%-6.2%). During complex walking, percentage errors were higher (13.0%-15.5%), intraclass correlation coefficients were 0.88-0.90, and 95% confidence intervals were 0.69-0.96. In the free-living environment, the absolute percentage difference between monitor counts was 25.4%, but the counts had a nearly perfect linear relationship.

CONCLUSION

Both monitors accurately counted steps during forward linear walking. StepWatch appears to be more accurate than FitBit during complex walking but a larger sample size may confirm these findings. FitBit consistently counted fewer steps than StepWatch during free-living walking. Clinical relevance The StepWatch and FitBit are acceptable tools for assessing forward, linear walking for individuals with transtibial amputation. Given the results' consistenty in the free-living enviorment, both tools may ultimiately be able to be used to count steps in the real world, but more research is needed to confirm these findings.

Energy expenditure associated with walking speed and angle of turn in children

Purpose

Recent studies have suggested that turning is power intensive. Given the sporadic and irregular movement patterns of children, such findings have important implications for the assessment of true energy expenditure associated with habitual physical activity. The purpose of this study was to investigate the influence of walking speed and angle, and their interaction, on the energy expenditure of healthy children.

Methods

20 children (10.1 ± 0.5 years; 10 boys) participated in the study. On two separate days, participants completed a turning protocol involving 3-min bouts of walking at one of the 16 speed (2.5, 3.5, 4.5, and 5.5 km h^− 1) and angle (0°, 45°, 90°, and 180°) combinations, interspersed by 3 min seated rest. The movement involved 5 m straight walking interspaced with prescribed turns with speed dictated by a digital, auditory metronome. Breath-by-breath gas exchange was measured, in addition to tri-axial acceleration and magnetic field intensity recorded at 100 Hz.

Results

Mixed models revealed a significant main effect for speed (p < 0.006) and angle (p < 0.006), with no significant interaction between speed and angle (p > 0.006). Significant differences to straight-line walking energy expenditure within speed were established for 3.5 and 5.5 km h^− 1 for 180° turns (~ 13% and ~ 30% increase, respectively).

Conclusion

These findings highlight the importance of accounting for the magnitude and frequency of turns completed when estimating children’s habitual physical activity and have significant implications for the assessment of daily energy expenditure.

Accuracy of Activity Trackers in Parkinson Disease: Should We Prescribe Them?

BACKGROUND

Wearable, consumer-grade activity trackers have become widely available as a means of monitoring physical activity in the form of step counts. However, step counts may not be accurate in persons with Parkinson disease (PD) due to atypical gait characteristics.

OBJECTIVE

This study aimed to investigate the accuracy of 4 consumer-grade activity trackers in individuals with PD while ambulating during continuous and discontinuous walking tasks.

DESIGN

This study used a cross-sectional design.

METHODS

Thirty-three persons with PD (Hoehn & Yahr stages 1-3) donned 4 models of activity trackers on the less affected side of their bodies. Participants performed 2 continuous walking tasks (2-minute walk tests at comfortable and fast speeds) and 2 discontinuous walking tasks (a simulated household course and an obstacle negotiation course) in an outpatient setting. Bland-Altman plots and intraclass correlation coefficients [ICC(2,1)] were computed as a measure of agreement between actual steps taken (reference standard: video recording) and steps recorded by each tracker.

RESULTS

The accuracy of the activity trackers varied widely, with ICCs ranging from -0.03 to 0.98. Overall, the most accurate device across all tasks was the Fitbit Zip, and the least accurate was the Jawbone Up Move during the simulated household course. All activity trackers were more accurate for continuous walking tasks compared with discontinuous walking tasks. Waist-mounted devices were more accurate than wrist-mounted devices with continuous tasks. Bland-Altman plots revealed that all activity trackers underestimated step counts.

LIMITATIONS

All walking tasks were measured over relatively short distances.

CONCLUSIONS

In persons with mild-to-moderate PD, waist-worn activity trackers may be prescribed to monitor bouts of continuous walking with reasonable accuracy; however, activity trackers have little utility in monitoring discontinuous walking common in household settings.

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2–4.2% depending on the type of wrist activities.

A comprehensive comparison of simple step counting techniques using wrist- and ankle-mounted accelerometer and gyroscope signals

The goal of this work is to compare the differences between various step counting algorithms using both accelerometer and gyroscope measurements from wrist and ankle-mounted sensors. Participants completed four different conditions on a treadmill while wearing an accelerometer and gyroscope on the wrist and the ankle. Three different step counting techniques were applied to the data from each sensor type and mounting location. It was determined that using gyroscope measurements allowed for better performance than the typically used accelerometers, and that ankle-mounted sensors provided better performance than those mounted on the wrist.

Prediction of sustained harmonic walking in the free-living environment using raw accelerometry data

OBJECTIVE

Using raw, sub-second-level accelerometry data, we propose and validate a method for identifying and characterizing walking in the free-living environment. We focus on sustained harmonic walking (SHW), which we define as walking for at least 10 s with low variability of step frequency.

APPROACH

We utilize the harmonic nature of SHW and quantify the local periodicity of the tri-axial raw accelerometry data. We also estimate the fundamental frequency of the observed signals and link it to the instantaneous walking (step-to-step) frequency (IWF). Next, we report the total time spent in SHW, number and durations of SHW bouts, time of the day when SHW occurred, and IWF for 49 healthy, elderly individuals.

MAIN RESULTS

The sensitivity of the proposed classification method was found to be 97%, while specificity ranged between 87% and 97% and the prediction accuracy ranged between 94% and 97%. We report the total time in SHW between 140 and 10 min d^-1 distributed between 340 and 50 bouts. We estimate the average IWF to be 1.7 steps-per-second.

SIGNIFICANCE

We propose a simple approach for the detection of SHW and estimation of IWF, based on Fourier decomposition.

Gait Complexity and Regularity Are Differently Modulated by Treadmill Walking in Parkinson's Disease and Healthy Population

Variability raises considerable interest as a promising and sensitive marker of dysfunction in physiology, in particular in neurosciences. Both internally (e.g., pathology) and/or externally (e.g., environment) generated perturbations and the neuro-mechanical responses to them contribute to the fluctuating dynamics of locomotion. Defective internal gait control in Parkinson's disease (PD), resulting in typical timing gait disorders, is characterized by the breakdown of the temporal organization of stride duration variability. Influence of external cue on gait pattern could be detrimental or advantageous depending on situations (healthy or pathological gait pattern, respectively). As well as being an interesting rehabilitative approach in PD, treadmills are usually implemented in laboratory settings to perform instrumented gait analysis including gait variability assessment. However, possibly acting as an external pacemaker, treadmill could modulate the temporal organization of gait variability of PD patients which could invalidate any gait variability assessment. This study aimed to investigate the immediate influence of treadmill walking (TW) on the temporal organization of stride duration variability in PD and healthy population. Here, we analyzed the gait pattern of 20 PD patients and 15 healthy age-matched subjects walking on overground and on a motorized-treadmill (randomized order) at a self-selected speed. The temporal organization and regularity of time series of walking were assessed on 512 consecutive strides and assessed by the application of non-linear mathematical methods (i.e., the detrended fluctuation analysis and power spectral density; and sample entropy, for the temporal organization and regularity of gait variability, respectively). A more temporally organized and regular gait pattern seems to emerge from TW in PD while no influence was observed on healthy gait pattern. Treadmill could afford the necessary framework to regulate gait rhythmicity in PD. Overall, the results support the hypothesis of a greater dependence to regulatory inputs as an explanatory factor of treadmill influence observed in PD. Also, since treadmill misrepresents the gait as more healthy than it is, the present findings underline that gait analysis using treadmill devices should be cautiously considered in PD and especially for gait variability assessment in gait lab.

Flexible Piezoelectric Sensor-Based Gait Recognition

Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

John Charnley Award: Randomized Clinical Trial of Direct Anterior and Miniposterior Approach THA: Which Provides Better Functional Recovery?

BACKGROUND

The choice of surgical approach for THA remains controversial. Some studies suggest that the direct anterior approach (DAA) leads to less muscle damage than the miniposterior approach (MPA), but there is little high-quality evidence indicating whether this accelerates recovery, or whether this approach-which may be technically more demanding-is associated with component malposition or more complications.

QUESTIONS/PURPOSES

(1) Does the DAA result in faster return to activities of daily living than the MPA? (2) Does the DAA have superior patient-reported outcome measures than the MPA? (3) Does the DAA result in improved radiographic outcomes than the MPA? (4) Does the DAA have a higher risk of complications than the MPA?

METHODS

Between March 1, 2013, and May 31, 2016, 116 patients undergoing primary unilateral THA were randomized to either the DAA or MPA; 15 patients withdrew after randomization, and one died 6 months after surgery from a stroke unrelated to the procedure. Recruitment stopped when 52 patients had been randomized into the DAA group and 49 in the MPA group (n = 101). After patient randomization, one high-volume surgeon performed all of the DAAs and three high-volume surgeons performed the MPA THAs. The groups did not differ in age (65 years; SD 11; range, 38-86 years), sex (52% women), or body mass index (mean 29 kg/m; SD 6 kg/m; range, 21-40 kg/m; all p > 0.40). Functional results included time to discontinue gait aids, discontinue all narcotics, and independence with various activities of daily living; accelerometer data evaluated activity level. Clinical and radiographic outcomes, Hip disability and Osteoarthritis Outcome Score, SF-12, and Harris hip scores to 1 year were also tabulated. The minimum followup was 365 days (mean ± SD, 627 ± 369 days).

RESULTS

There were slight differences in early functional recovery that favored the DAA versus the MPA: time to discontinue walker use (10 versus 15 days, p = 0.01) and time to discontinue all gait aids (17 versus 24 days, p = 0.04). There were no other differences in early functional milestones, although at 2 weeks after surgery, mean steps per day were 3897 (SD 2258; range, 737-11,010) for the DAA versus 2235 for the MPA (SD 1688; range, 27-7450; p < 0.01). There was no difference in activity monitoring at 1 year. There were no differences in patient-reported outcome scores between the groups. There was no difference in the radiographic parameters measured in the two groups, including leg length discrepancy, component position, or offset, and there was no subsidence observed in any hip. There was no difference in complications between the DAA and the MPA groups (8% [four of 52] versus 10% [five of 49]; p = 0.33).

CONCLUSIONS

Both the DAA and MPA approaches provided excellent early recovery with a low risk of complications. Patients undergoing the DAA had a slightly faster recovery, as measured by milestones of function and quantified by activity monitor data, but no substantive differences were evident at 2 months. Because the DAA is the less studied approach, longer term (> 1 year) complications may yet accrue, will be important to quantify, and may offset early benefits.

LEVEL OF EVIDENCE

Level I, therapeutic study.

An adaptive, real-time cadence algorithm for unconstrained sensor placement

This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods. 22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s^-1), running (3.4 m s^-1), and sprinting (5.2 and 5.7 m s^-1), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm. The described algorithm resulted in consistent numbers of true positives (85.6-100.0%) and false positives (0.0-2.9%) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ± SD: 98.9 ± 0.2%), compared to conventional cross-correlation (87.3 ± 13.5%), biased (73.0 ± 16.2) and unbiased (82.2 ± 20.6) autocorrelation procedures. This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions.

The bit doesn't fit: Evaluation of a commercial activity-tracker at slower walking speeds

Accelerometer-based commercial activity trackers are a low-cost and convenient method for monitoring and assessing health measures such as gait. However, the accuracy of these activity trackers in slow walking conditions on a minute-by-minute basis is largely unknown. In this study, the accuracy of a hip-worn commercial activity tracker (FitBit Ultra) was examined through step counts. Accuracy was evaluated through four minute trials of treadmill walking at speeds representative of older adults (0.9, 1.1, and 1.3m/s). Minute-by-minute step count was extracted through the FitBit API and compared it to observer counted steps through video recordings. Results highlighted a significant over-reporting of steps at the highest speed, and a significant under-reporting of steps at the slowest speed, with the FitBit Ultra failing to count steps for one or more minutes at the slowest speed for 11 participants. This study highlights problems with using the FitBit Ultra by slow-walking populations, and recommends that researchers and clinicians should carefully consider the trade-off between accuracy and convenience when using commercial activity trackers with slow-walking populations.

Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket

The assessment of spatiotemporal gait parameters is a useful clinical indicator of health status. Unfortunately, most assessment tools require controlled laboratory environments which can be expensive and time consuming. As smartphones with embedded sensors are becoming ubiquitous, this technology can provide a cost-effective, easily deployable method for assessing gait. Therefore, the purpose of this study was to assess the reliability and validity of a smartphone-based accelerometer in quantifying spatiotemporal gait parameters when attached to the body or in a bag, belt, hand, and pocket. Thirty-four healthy adults were asked to walk at self-selected comfortable, slow, and fast speeds over a 10-m walkway while carrying a smartphone. Step length, step time, gait velocity, and cadence were computed from smartphone-based accelerometers and validated with GAITRite. Across all walking speeds, smartphone data had excellent reliability (ICC_2,1≥0.90) for the body and belt locations, with bag, hand, and pocket locations having good to excellent reliability (ICC_2,1≥0.69). Correlations between the smartphone-based and GAITRite-based systems were very high for the body (r=0.89, 0.98, 0.96, and 0.87 for step length, step time, gait velocity, and cadence, respectively). Similarly, Bland-Altman analysis demonstrated that the bias approached zero, particularly in the body, bag, and belt conditions under comfortable and fast speeds. Thus, smartphone-based assessments of gait are most valid when placed on the body, in a bag, or on a belt. The use of a smartphone to assess gait can provide relevant data to clinicians without encumbering the user and allow for data collection in the free-living environment.

A pilot study of physical activity and sedentary behavior distribution patterns in older women

The study aims were to investigate free-living physical activity and sedentary behavior distribution patterns in a group of older women, and assess the cross-sectional associations with body mass index (BMI). Eleven older women (mean (SD) age: 77 (9) yrs) wore custom-built activity monitors, each containing a tri-axial accelerometer (±16g, 100Hz), on the waist and ankle for lab-based walking trials and 4 days in free-living. Daily active time, step counts, cadence, and sedentary break number were estimated from acceleration data. The sedentary bout length distribution and sedentary time accumulation pattern, using the Gini index, were investigated. Associations of the parameters' total daily values and coefficients of variation (CVs) of their hourly values with BMI were assessed using linear regression. The algorithm demonstrated median sensitivity, positive predictive value, and agreement values >98% and <1% mean error in cadence calculations with video identification during lab trials. Participants' sedentary bouts were found to be power law distributed with 56% of their sedentary time occurring in 20min bouts or longer. Meaningful associations were detectable in the relationships of total active time, step count, sedentary break number and their CVs with BMI. Active time and step counts had moderate negative associations with BMI while sedentary break number had a strong negative association. Active time, step count and sedentary break number CVs also had strong positive associations with BMI. The results highlight the importance of measuring sedentary behavior and suggest a more even distribution of physical activity throughout the day is associated with lower BMI.

The impact of accelerometer wear location on the relationship between step counts and arterial stiffness in adults treated for hypertension and diabetes

OBJECTIVES

Accelerometer placement at the wrist is convenient and increasingly adopted despite less accurate physical activity (PA) measurement than with waist placement. Capitalizing on a study that started with wrist placement and shifted to waist placement, we compared associations between PA measures derived from different accelerometer locations with a responsive arterial health indicator, carotid-femoral pulse wave velocity (cfPWV).

DESIGN

Cross-sectional study.

METHODS

We previously demonstrated an inverse association between waist-worn pedometer-assessed step counts (Yamax SW-200, 7 days) and cfPWV (-0.20m/s, 95% CI -0.28, -0.12 per 1000 step/day increment) in 366 adults. Participants concurrently wore accelerometers (ActiGraph GT3X+), most at the waist but the first 46 at the wrist. We matched this subgroup with participants from the 'waist accelerometer' group (sex, age, and pedometer-assessed steps/day) and assessed associations with cfPWV (applanation tonometry, Sphygmocor) separately in each subgroup through linear regression models.

RESULTS

Compared to the waist group, wrist group participants had higher step counts (mean difference 3980 steps/day; 95% CI 2517, 5443), energy expenditure (967kcal/day, 95% CI 755, 1179), and moderate-to-vigorous-PA (138min; 95% CI 114, 162). Accelerometer-assessed step counts (waist) suggested an association with cfPWV (-0.28m/s, 95% CI -0.58, 0.01); but no relationship was apparent with wrist-assessed steps (0.02m/s, 95% CI -0.24, 0.27).

CONCLUSIONS

Waist but not wrist ActiGraph PA measures signal associations between PA and cfPWV. We urge researchers to consider the importance of wear location choice on relationships with health indicators.

How Accurate Is Your Activity Tracker? A Comparative Study of Step Counts in Low-Intensity Physical Activities

Background

As commercially available activity trackers are being utilized in clinical trials, the research community remains uncertain about reliability of the trackers, particularly in studies that involve walking aids and low-intensity activities. While these trackers have been tested for reliability during walking and running activities, there has been limited research on validating them during low-intensity activities and walking with assistive tools.

Objective

The aim of this study was to (1) determine the accuracy of 3 Fitbit devices (ie, Zip, One, and Flex) at different wearing positions (ie, pants pocket, chest, and wrist) during walking at 3 different speeds, 2.5, 5, and 8 km/h, performed by healthy adults on a treadmill; (2) determine the accuracy of the mentioned trackers worn at different sites during activities of daily living; and (3) examine whether intensity of physical activity (PA) impacts the choice of optimal wearing site of the tracker.

Methods

We recruited 15 healthy young adults to perform 6 PAs while wearing 3 Fitbit devices (ie, Zip, One, and Flex) on their chest, pants pocket, and wrist. The activities include walking at 2.5, 5, and 8 km/h, pushing a shopping cart, walking with aid of a walker, and eating while sitting. We compared the number of steps counted by each tracker with gold standard numbers. We performed multiple statistical analyses to compute descriptive statistics (ie, ANOVA test), intraclass correlation coefficient (ICC), mean absolute error rate, and correlation by comparing the tracker-recorded data with that of the gold standard.

Results

All the 3 trackers demonstrated good-to-excellent (ICC>0.75) correlation with the gold standard step counts during treadmill experiments. The correlation was poor (ICC<0.60), and the error rate was significantly higher in walker experiment compared to other activities. There was no significant difference between the trackers and the gold standard in the shopping cart experiment. The wrist worn tracker, Flex, counted several steps when eating (P<.01). The chest tracker was identified as the most promising site to capture steps in more intense activities, while the wrist was the optimal wearing site in less intense activities.

Conclusions

This feasibility study focused on 6 PAs and demonstrated that Fitbit trackers were most accurate when walking on a treadmill and least accurate during walking with a walking aid and for low-intensity activities. This may suggest excluding participants with assistive devices from studies that focus on PA interventions using commercially available trackers. This study also indicates that the wearing site of the tracker is an important factor impacting the accuracy performance. A larger scale study with a more diverse population, various activity tracker vendors, and a larger activity set are warranted to generalize our results.

Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents

BACKGROUND

Smart wearables such as the Fitbit wristband provide the opportunity to monitor patients more comprehensively, to track patients in a fashion that more closely follows the contours of their lives, and to derive a more complete dataset that enables precision medicine. However, the utility and efficacy of using wearable devices to monitor adolescent patients' asthma outcomes have not been established.

OBJECTIVE

The objective of this study was to explore the association between self‑reported sleep data, Fitbit sleep and physical activity data, and pediatric asthma impact (PAI).

METHODS

We conducted an 8‑week pilot study with 22 adolescent asthma patients to collect: (1) weekly or biweekly patient‑reported data using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of PAI, sleep disturbance (SD), and sleep‑related impairment (SRI) and (2) real-time Fitbit (ie, Fitbit Charge HR) data on physical activity (F-AM) and sleep quality (F‑SQ). To explore the relationship among the self-reported and Fitbit measures, we computed weekly Pearson correlations among these variables of interest.

RESULTS

We have shown that the Fitbit-derived sleep quality F-SQ measure has a moderate correlation with the PROMIS SD score (average r=-.31, P=.01) and a weak but significant correlation with the PROMIS PAI score (average r=-.18, P=.02). The Fitbit physical activity measure has a negligible correlation with PAI (average r=.04, P=.62).

CONCLUSIONS

Our findings support the potential of using wrist-worn devices to continuously monitor two important factors-physical activity and sleep-associated with patients' asthma outcomes and to develop a personalized asthma management platform.

Mobile Device Accuracy for Step Counting Across Age Groups

Background

Only one in five American meets the physical activity recommendations of the Department of Health and Human Services. The proliferation of wearable devices and smartphones for physical activity tracking has led to an increasing number of interventions designed to facilitate regular physical activity, in particular to address the obesity epidemic, but also for cardiovascular disease patients, cancer survivors, and older adults. However, the inconsistent findings pertaining to the accuracy of wearable devices for step counting needs to be addressed, as well as factors known to affect gait (and thus potentially impact accuracy) such as age, body mass index (BMI), or leading arm.

Objective

We aim to assess the accuracy of recent mobile devices for counting steps, across three different age groups.

Methods

We recruited 60 participants in three age groups: 18-39 years, 40-64 years, and 65-84 years, who completed two separate 1000 step walks on a treadmill at a self-selected speed between 2 and 3 miles per hour. We tested two smartphones attached on each side of the waist, and five wrist-based devices worn on both wrists (2 devices on one wrist and 3 devices on the other), as well as the Actigraph wGT3X-BT, and swapped sides between each walk. All devices were swapped dominant-to-nondominant side and vice-versa between the two 1000 step walks. The number of steps was recorded with a tally counter. Age, sex, height, weight, and dominant hand were self-reported by each participant.

Results

Among the 60 participants, 36 were female (60%) and 54 were right-handed (90%). Median age was 53 years (min=19, max=83), median BMI was 24.1 (min=18.4, max=39.6). There was no significant difference in left- and right-hand step counts by device. Our analyses show that the Fitbit Surge significantly undercounted steps across all age groups. Samsung Gear S2 significantly undercounted steps only for participants among the 40-64 year age group. Finally, the Nexus 6P significantly undercounted steps for the group ranging from 65-84 years.

Conclusions

Our analysis shows that apart from the Fitbit Surge, most of the recent mobile devices we tested do not overcount or undercount steps in the 18-39-year-old age group, however some devices undercount steps in older age groups. This finding suggests that accuracy in step counting may be an issue with some popular wearable devices, and that age may be a factor in undercounting. These results are particularly important for clinical interventions using such devices and other activity trackers, in particular to balance energy requirements with energy expenditure in the context of a weight loss intervention program.

Principal component analysis for ataxic gait using a triaxial accelerometer

Background

It is quite difficult to evaluate ataxic gait quantitatively in clinical practice. The aim of this study was to analyze the characteristics of ataxic gait using a triaxial accelerometer and to develop a novel biomarker of integrated gate parameters for ataxic gait.

Methods

Sixty-one patients with spinocerebellar ataxia (SCA) or multiple system atrophy with predominant cerebellar ataxia (MSA-C) and 57 healthy control subjects were enrolled. The subjects were instructed to walk 10 m for a total of 12 times on a flat floor at their usual walking speed with a triaxial accelerometer attached to their back. Gait velocity, cadence, step length, step regularity, step symmetry, and degree of body sway were evaluated. Principal component analysis (PCA) was used to analyze the multivariate gait parameters. The Scale for the Assessment and Rating of Ataxia (SARA) was evaluated on the same day of the 10-m walk trial.

Results

PCA divided the gait parameters into four principal components in the controls and into two principal components in the patients. The four principal components in the controls were similar to those found in earlier studies. The second principal component in the patients had relevant factor loading values for gait velocity, step length, regularity, and symmetry in addition to the degree of body sway in the medio-lateral direction. The second principal component score (PCS) in the patients was significantly correlated with disease duration and the SARA score of gait (ρ = −0.363, p = 0.004; ρ = −0.574, p < 0.001, respectively).

Conclusions

PCA revealed the main component of ataxic gait. The PCS of the main component was significantly different between the patients and controls, and it was well correlated with disease duration and the SARA score of gait in the patients. We propose that this score provides a novel method to assess the severity of ataxic gait quantitatively using a triaxial accelerometer.

A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices. A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover. On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Clinical validation of a body-fixed 3D accelerometer and algorithm for activity monitoring in orthopaedic patients

Background/Objective

Activity is increasingly being recognized as a highly relevant parameter in all areas of healthcare for diagnosis, treatment, or outcome assessment, especially in orthopaedics where the movement apparatus is directly affected. Therefore, the aim of this study was to develop, describe, and clinically validate a generic activity-monitoring algorithm, satisfying a combination of three criteria. The algorithm must be able to identify, count, and time a large set of relevant daily activities. It must be validated for orthopaedic patients as well as healthy individuals, and the validation must be in a setting that mimics free-living conditions.

Methods

Using various technical solutions, such as a dual-axis approach, dynamic inclinometry (hip flexion), and semiautomatic calibration (gait speed), the algorithms were designed to count and time the following postures, transfers, and activities of daily living: resting/sitting, standing, walking, ascending and descending stairs, sit-stand transitions, and cycling. In addition, the number of steps per walking bout was determined. Validation was performed with healthy individuals and patients who had undergone unilateral total joint arthroplasty, representing a wide spectrum of functional capacity. Video observation was used as the gold standard to count and time activities in a validation protocol approaching free-living conditions.

Results

In total 992 and 390 events (activities or postures) were recorded in the healthy group and patient group, respectively. The mean error varied between 0% and 2.8% for the healthy group and between 0% and 7.5% for the patient group. The error expressed in percentage of time varied between 2.0% and 3.0% for both groups.

Conclusion

Activity monitoring of orthopaedic patients by counting and timing a large set of relevant daily life events is feasible in a user- and patient-friendly way and at high clinical validity using a generic three-dimensional accelerometer and algorithms based on empirical and physical methods. The algorithms performed well for healthy individuals as well as patients recovering after total joint replacement in a challenging validation set-up. With such a simple and transparent method real-life activity parameters can be collected in orthopaedic practice for diagnostics, treatments, outcome assessment, or biofeedback.

Does Nordic Walking restore the temporal organization of gait variability in Parkinson's disease?

BACKGROUND

Gait disorders of Parkinson's disease (PD) are characterized by the breakdown of the temporal organization of stride duration variability that was tightly associated to dynamic instability in PD. Activating the upper body during walking, Nordic Walking (NW) may be used as an external cueing to improve spatiotemporal parameters of gait, such as stride length or gait variability, in PD. The aim of this study was to evaluate the beneficial effects of NW on temporal organization of gait variability and spatiotemporal gait variables in PD.

METHODS

Fourteen mild to moderate PD participants and ten age-matched healthy subjects performed 2 × 12 min overground walking sessions (with and without pole in a randomized order) at a comfortable speed. Gait speed, cadence, step length and temporal organization (i.e. long-range autocorrelations; LRA) of stride duration variability were studied on 512 consecutive gait cycles using a unidimensional accelerometer placed on the malleola of the most affected side in PD patients and of the dominant side in healthy controls. The presence of LRA was determined using the Rescaled Range Analysis (Hurst exponent) and the Power Spectral Density (α exponent). To assess NW and disease influences on gait, paired t-tests, Z-score and a two-way (pathological condition x walking condition) ANOVA repeated measure were used.

RESULTS

Leading to significant improvement of LRA, NW enhances step length and reduces gait cadence without any change in gait speed in PD. Interestingly, LRA and step length collected from the NW session are similar to that of the healthy population.

CONCLUSION

This cross-sectional controlled study demonstrates that NW may constitute a powerful way to struggle against the randomness of PD gait and the typical gait hypokinesia. Involving a voluntary intersegmental coordination, such improvement could also be due to the upper body rhythmic movements acting as rhythmical external cue to bypass their defective basal ganglia circuitries.

ETHICS COMMITTEE'S REFERENCE NUMBER

B403201318916 TRIAL REGISTRATION: NCT02419768.

Comparison between Mother, ActiGraph wGT3X-BT, and a hand tally for measuring steps at various walking speeds under controlled conditions

Introduction

Walking is endorsed as health enhancing and is the most common type of physical activity among older adults. Accelerometers are superior to self-reports when measuring steps, however, if they are to be used by clinicians the validity is of great importance. The aim of this study was to investigate the criterion validity of Mother and ActiGraph wGT3X-BT in measuring steps by comparing the devices to a hand tally under controlled conditions in healthy participants.

Methods

Thirty healthy participants were fitted with a belt containing the sensor of Mother (Motion Cookie) and ActiGraph. Participants walked on a treadmill for two minutes at each of the following speeds; 3.2, 4.8, and 6.4 km/h. The treadmill walking was video recorded and actual steps were subsequently determined by using a hand tally. Wilcoxon’s signed ranks test was used to determine whether Mother and ActiGraph measured an identical number of steps compared to the hand tally. Intraclass correlation coefficients were calculated to determine the relationship and Root Mean Square error was calculated to investigate the average error between the devices and the hand tally. Percent differences (PD) were calculated for between-instrument agreement (Mother vs. the hand tally and ActiGraph vs. the hand tally) and PDs below 3% were interpreted as acceptable and clinically irrelevant.

Results

Mother and ActiGraph under-counted steps significantly compared to the hand tally at all walking speeds (p < 0.001). Mother had a median of total differences of 9.5 steps (IQR = 10) and ActiGraph 59 steps (IQR = 77). Mother had smaller PDs at all speeds especially at 3.2 km/h (2.5% compared to 26.7%). Mother showed excellent ICC values ≥0.88 (0.51–0.96) at all speeds whilst ActiGraph had poor and fair to good ICC values ranging from 0.03 (−0.09–0.21) at a speed of 3.2 km/h to 0.64 (0.16–0.84) at a speed of 6.4 km/h.

Conclusion

Mother provides valid measures of steps at walking speeds of 3.2, 4.8, and 6.4 km/h with clinically irrelevant deviations compared to a hand tally while ActiGraph only provides valid measurements at 6.4 km/h based on the 3% criterion. These results have significant potential for valid objective measurements of low walking speeds. However, further research should investigate the validity of Mother in patients at even slower walking speeds and in free-living conditions.

Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis

Research suggests wearables and not instrumented walkways are better suited to quantify gait outcomes in clinic and free-living environments, providing a more comprehensive overview of walking due to continuous monitoring. Numerous validation studies in controlled settings exist, but few have examined the validity of wearables and associated algorithms for identifying and quantifying step counts and walking bouts in uncontrolled (free-living) environments. Studies which have examined free-living step and bout count validity found limited agreement due to variations in walking speed, changing terrain or task. Here we present a gait segmentation algorithm to define free-living step count and walking bouts from an open-source, high-resolution, accelerometer-based wearable (AX3, Axivity). Ten healthy participants (20-33 years) wore two portable gait measurement systems; a wearable accelerometer on the lower-back and a wearable body-mounted camera (GoPro HERO) on the chest, for 1 h on two separate occasions (24 h apart) during free-living activities. Step count and walking bouts were derived for both measurement systems and compared. For all participants during a total of almost 20 h of uncontrolled and unscripted free-living activity data, excellent relative (rho  ⩾  0.941) and absolute (ICC_(2,1)  ⩾  0.975) agreement with no presence of bias were identified for step count compared to the camera (gold standard reference). Walking bout identification showed excellent relative (rho  ⩾  0.909) and absolute agreement (ICC_(2,1)  ⩾  0.941) but demonstrated significant bias. The algorithm employed for identifying and quantifying steps and bouts from a single wearable accelerometer worn on the lower-back has been demonstrated to be valid and could be used for pragmatic gait analysis in prolonged uncontrolled free-living environments.

Validation of a novel activity monitor in impaired, slow-walking, crutch-supported patients

BACKGROUND

A growing need in clinical practice of rehabilitation and orthopaedic medicine is for objective outcome tools to estimate physical activity. Current techniques show limited validity or are too demanding for routine clinical use. Accelerometer-based activity monitors (AMs) have shown promise for measuring physical activity in healthy people but lack validity in impaired patients.

OBJECTIVES

This study aimed to validate an accelerometer-based AM in impaired, slow-walking, crutch-supported patients after total joint arthroplasty (TJA).

METHODS

Shortly after TJA, patients who were safely mobilized with 2 crutches and 8 healthy participants completed a trial of different activities while wearing the AM on the lateral upper leg and being videotaped. Outcome variables (e.g., time walking, number of gait cycles, sit-stand-sit transfers) were compared to video recordings, and sensitivity, predictive value and mean percentage difference (MPD) values were calculated.

RESULTS

We included 40 patients (mean age: 65±9 years; mean BMI: 30±6kg/m²; male:female ratio: 18:22) and 8 healthy participants (mean age: 49±20 years; mean BMI: 23±0.7kg/m²; male:female ratio: 5:3). The AM showed excellent sensitivity (>95%) and predictive value (>95%) in identifying activities (e.g., walking, sitting, resting) and detecting the number of gait cycles and sit-stand-sit transfers (mean percentage difference: ±2%). Detection of number of steps ascending and descending stairs and cadence was more difficult but still showed good results (mean percentage difference: ±7%).

CONCLUSIONS

This is the first validation study to assess physical activity with an AM in impaired, slow-walking, crutch-supported patients. The AM was a valid tool for measuring physical activity in these patients. The tool may help in evaluating and optimizing rehabilitation programs for patients after TJA, those recovering from stroke or chronic impaired patients.

Sensor-based foot-mounted wearable system and pressure sensitive gait analysis : Agreement in frail elderly people in long-term care

BACKGROUND

The RehaWatch® system is a portable accelerometer for measurement of gait parameters that shows good validity in young adults; however, validity data are missing for elderly persons in long-term care (LTC).

AIM

The aim was to evaluate the concurrent validity of the RehaWatch® system using the GAITRite® system as a criterion reference for gait assessment in the LTC elderly.

MATERIAL AND METHODS

In this study 23 elderly participants (mean age 90.9 ± 8.4 years) performed 4 walking trials at normal and fast walking speed during single task and dual task walking. Data for both systems were collected simultaneously for each trial. Concurrent validity was assessed through limits of agreement (LoA) methodology using Bland-Altman plots.

RESULTS

No systematic bias could be determined. Mean biases for step duration, velocity and cadence were above the prespecified ±7 % value from zero lines for normal walking during single task and dual task walking. The LoA had a wide range between -21 % and 25 %. Only cadence showed small LoA for normal walking speed during single (-8.4 % to 7.7 %) and dual tasking (-4.1 % to 3 %). Heterogeneous bias was determined for step duration during fast walking during dual task and for velocity during fast walking during single task and dual task. Heteroscedasticity was shown for step length during normal walking under the dual task condition and fast walking during single task and dual task activities.

CONCLUSION

No gait parameters are interchangeably usable between the two systems for normal walking during single task and dual task activities.

Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors

This paper proposes a novel method of estimating walking distance based on a precise counting of walking strides using insole sensors. We use an inertial triaxial accelerometer and eight pressure sensors installed in the insole of a shoe to record walkers' movement data. The data is then transmitted to a smartphone to filter out noise and determine stance and swing phases. Based on phase information, we count the number of strides traveled and estimate the movement distance. To evaluate the accuracy of the proposed method, we created two walking databases on seven healthy participants and tested the proposed method. The first database, which is called the short distance database, consists of collected data from all seven healthy subjects walking on a 16 m distance. The second one, named the long distance database, is constructed from walking data of three healthy subjects who have participated in the short database for an 89 m distance. The experimental results show that the proposed method performs walking distance estimation accurately with the mean error rates of 4.8% and 3.1% for the short and long distance databases, respectively. Moreover, the maximum difference of the swing phase determination with respect to time is 0.08 s and 0.06 s for starting and stopping points of swing phases, respectively. Therefore, the stride counting method provides a highly precise result when subjects walk.

Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis

BACKGROUND

Wearable activity trackers are promising as interventions that offer guidance and support for increasing physical activity and health-focused tracking. Most adults do not meet their recommended daily activity guidelines, and wearable fitness trackers are increasingly cited as having great potential to improve the physical activity levels of adults.

OBJECTIVE

The objective of this study was to use the Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy to examine if the design of wearable activity trackers incorporates behavior change techniques (BCTs). A secondary objective was to critically analyze whether the BCTs present relate to known drivers of behavior change, such as self-efficacy, with the intention of extending applicability to older adults in addition to the overall population.

METHODS

Wearing each device for a period of 1 week, two independent raters used CALO-RE taxonomy to code the BCTs of the seven wearable activity trackers available in Canada as of March 2014. These included Fitbit Flex, Misfit Shine, Withings Pulse, Jawbone UP24, Spark Activity Tracker by SparkPeople, Nike+ FuelBand SE, and Polar Loop. We calculated interrater reliability using Cohen's kappa.

RESULTS

The average number of BCTs identified was 16.3/40. Withings Pulse had the highest number of BCTs and Misfit Shine had the lowest. Most techniques centered around self-monitoring and self-regulation, all of which have been associated with improved physical activity in older adults. Techniques related to planning and providing instructions were scarce.

CONCLUSIONS

Overall, wearable activity trackers contain several BCTs that have been shown to increase physical activity in older adults. Although more research and development must be done to fully understand the potential of wearables as health interventions, the current wearable trackers offer significant potential with regard to BCTs relevant to uptake by all populations, including older adults.

Predicting daily gait behaviors after anterior cruciate ligament surgery: A case study

After surgical interventions such as anterior cruciate ligament reconstruction (ACLr), people exhibit altered gait mechanics due to joint impairments. Persistence of altered mechanics after resolution of impairments may be related to daily reinforcement of maladaptive behavior. Quantifying the contribution of such maladaptive motor strategies requires continuous monitoring of locomotor behaviors in the home setting. In this paper, we investigate an inertial sensor based approach to monitoring ambient activities. We evaluate the relative performance of our predictive algorithm on one control and one individual post-ACL reconstruction.

Step detection using multi- versus single tri-axial accelerometer-based systems

Multiple sensors are often considered necessary for increased step count accuracy. However, subject adherence to device-wear increases using a minimal number of activity monitors (AMs). The study aims were to determine and compare the validity of using multiple AMs versus a single AM to detect steps by comparison to video using a modification of an algorithm previously developed for a four-accelerometer AM system capable, unlike other algorithms, of accurate step detection for gait velocities as low as 0.1 m s(-1). Twelve healthy adults wore ankle, thigh and waist AMs while performing walking/jogging trials at gait velocities from 0.1-4.8 m s(-1) and a simulated free-living dynamic activities protocol. Nineteen older adults wore ankle and waist AMs while walking at velocities from 0.5-2.0 m s(-1). As little as one AM (thigh or waist) accurately detected steps for velocities  >0.5 m s(-1). A single ankle AM accurately detected steps for velocities  ⩾0.1 m s(-1). Only the thigh AM could not accurately detect steps during the dynamic activities. Only the thigh-ankle combination or single waist AM could accurately distinguish between walking and jogging steps. These laboratory-based results suggest that the presented algorithm can accurately detect steps in a free-living environment using only one ankle or waist AM.

Reliability and validity of ten consumer activity trackers

Background

Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions.

Method

Healthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots.

Results

Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (−0.2, −0.4), Fibit Flex (−5.7, 3.7), Jawbone Up (−1.0, 1.4), Nike+ Fuelband (−18, −24), Misfit Shine (0.2, 1.1), Withings Pulse (−0.5, −7.9), Fitbit Zip (−0.3, 1.2), Omron (2.5, −0.4), Digiwalker (−1.2, −5.9), and Moves app (9.6, −37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition.

Conclusion

The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.

An Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations

BACKGROUND

Pedometers are considered desirable devices for monitoring physical activity. Two population groups of interest include patients having undergone surgery in the lower extremities or who are otherwise weakened through disease, medical treatment, or surgery procedures, as well as the slow walking senior population. For these population groups, pedometers must be able to perform reliably and accurately at slow walking speeds. The objectives of this study were to evaluate the step count accuracy of three commercially available pedometers, the Yamax (Tokyo, Japan) Digi-Walker(®) SW-200 (YM), the Omron (Kyoto, Japan) HJ-720 (OM), and the Fitbit (San Francisco, CA) Zip (FB), at slow walking speeds, specifically at 1, 2, and 3 km/h, and to raise awareness of the necessity of focusing research on step-counting devices and algorithms for slow walking populations.

MATERIALS AND METHODS

Fourteen participants 29.93 ±4.93 years of age were requested to walk on a treadmill at the three specified speeds, in four trials of 100 steps each. The devices were worn by the participants on the waist belt. The pedometer counts were recorded, and the error percentage was calculated.

RESULTS

The error rate of all three evaluated pedometers decreased with the increase of speed: at 1 km/h the error rates varied from 87.11% (YM) to 95.98% (FB), at 2 km/h the error rates varied from 17.27% (FB) to 46.46% (YM), and at 3 km/h the error rates varied from 22.46% (YM) to a slight overcount of 0.70% (FB).

CONCLUSIONS

It was observed that all the evaluated devices have high error rates at 1 km/h and mixed error rates at 2 km/h, and at 3 km/h the error rates are the smallest of the three assessed speeds, with the OM and the FB having a slight overcount. These results show that research on pedometers' software and hardware should focus more on accurate step detection at slow walking speeds.

Accurate walking and running speed estimation using wrist inertial data

In this work, we present an accelerometry-based device for robust running speed estimation integrated into a watch-like device. The estimation is based on inertial data processing, which consists in applying a leg-and-arm dynamic motion model to 3D accelerometer signals. This motion model requires a calibration procedure that can be done either on a known distance or on a constant speed period. The protocol includes walking and running speeds between 1.8km/h and 19.8km/h. Preliminary results based on eleven subjects are characterized by unbiased estimations with 2(nd) and 3(rd) quartiles of the relative error dispersion in the interval ±5%. These results are comparable to accuracies obtained with classical foot pod devices.

Instrumenting gait with an accelerometer: a system and algorithm examination

Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required.

Assessment of gait kinetics using triaxial accelerometers

Repeated durations of dynamic activity with high ground reaction forces (GRFs) and loading rates (LRs) can be beneficial to bone health. To fully characterize dynamic activity in relation to bone health, field-based measurements of gait kinetics are desirable to assess free-living lower-extremity loading. The study aims were to determine correlations of peak vertical GRF and peak vertical LR with ankle peak vertical accelerations, and of peak resultant GRF and peak resultant LR with ankle peak resultant accelerations, and to compare them to correlations with tibia, thigh, and waist accelerations. GRF data were collected as ten healthy subjects (26 [19-34] years) performed 8-10 walking trials at velocities ranging from 0.19 to 3.05 m/s while wearing ankle, tibia, thigh, and waist accelerometers. While peak vertical accelerations of all locations were positively correlated with peak vertical GRF and LR (r² > .53, P < .001), ankle peak vertical accelerations were the most correlated (r² > .75, P < .001). All peak resultant accelerations were positively correlated with peak resultant GRF and LR (r² > .57, P < .001), with waist peak resultant acceleration being the most correlated (r² > .70, P < .001). The results suggest that ankle or waist accelerometers give the most accurate peak GRF and LR estimates and could be useful tools in relating physical activity to bone health.

Monitoring and detection platform to prevent anomalous situations in home care

Monitoring and tracking people at home usually requires high cost hardware installations, which implies they are not affordable in many situations. This study/paper proposes a monitoring and tracking system for people with medical problems. A virtual organization of agents based on the PANGEA platform, which allows the easy integration of different devices, was created for this study. In this case, a virtual organization was implemented to track and monitor patients carrying a Holter monitor. The system includes the hardware and software required to perform: ECG measurements, monitoring through accelerometers and WiFi networks. Furthermore, the use of interactive television can moderate interactivity with the user. The system makes it possible to merge the information and facilitates patient tracking efficiently with low cost.

Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection

A robust method for identifying movement in the free-living environment is needed to objectively measure physical activity. The purpose of this study was to validate the identification of postural orientation and movement from acceleration data against visual inspection from video recordings. Using tri-axial accelerometers placed on the waist and thigh, static orientations of standing, sitting, and lying down, as well as dynamic movements of walking, jogging and transitions between postures were identified. Additionally, subjects walked and jogged at self-selected slow, comfortable, and fast speeds. Identification of tasks was performed using a combination of the signal magnitude area, continuous wavelet transforms and accelerometer orientations. Twelve healthy adults were studied in the laboratory, with two investigators identifying tasks during each second of video observation. The intraclass correlation coefficients for inter-rater reliability were greater than 0.95 for all activities except for transitions. Results demonstrated high validity, with sensitivity and positive predictive values of greater than 85% for sitting and lying, with walking and jogging identified at greater than 90%. The greatest disagreement in identification accuracy between the algorithm and video occurred when subjects were asked to fidget while standing or sitting. During variable speed tasks, gait was correctly identified for speeds between 0.1m/s and 4.8m/s. This study included a range of walking speeds and natural movements such as fidgeting during static postures, demonstrating that accelerometer data can be used to identify orientation and movement among the general population.