scholarly journals Validity of a Smart-Glasses-Based Step-Count Measure during Simulated Free-Living Conditions

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 404
Author(s):  
Alessia Cristiano ◽  
Alberto Sanna ◽  
Diana Trojaniello
Keyword(s):  
Inertial Sensors ◽  
Percentage Error ◽  
Free Living ◽  
Step Counter ◽  
Smart Glasses ◽  
Counting Functions ◽  
Step Counting ◽  
Key Features ◽  
Free Living Conditions ◽  
The Ideal

Step counting represents a valuable approach to monitor the amount of daily physical activity. The feet, wrist and trunk have been demonstrated as the ideal locations to automatically detect the number of steps through body-worn devices (i.e., step counters). Key features of such devices are high usability, practicality and unobtrusiveness. Therefore, the opportunity to integrate step-counting functions in daily worn accessories represents one of the recent and most important challenges. In this context, the present study aimed to investigate the validity of a smart-glasses-based step-counter measure by comparing their performances against the most popular commercial step counters. To this purpose, smart glasses data from 26 healthy subjects performing simulated free-living walking conditions along a predefined path were collected. Reference measures from inertial sensors mounted on the subjects’ ankles and data from commercial (waist- and wrists-worn) step counters were acquired during the tests. The results showed an overall percentage error of 1%. In conclusion, the proposed smart glasses could be considered an accurate step counter, showing performances comparable to the most common commercial step counters.

Validity, Reliability and Sensitivity to Change of Three Consumer-grade Activity Trackers in Controlled and Free-living Conditions among Older Adults (Preprint)

2021 ◽  
Author(s):  
Kaja Kastelic ◽  
Marina Dobnik ◽  
Stefan Loefler ◽  
Christian Hofer ◽  
Nejc Šarabon
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Intraclass Correlation ◽  
Sleep Time ◽  
Living Conditions ◽  
Percentage Error ◽  
Sensitivity To Change ◽  
Free Living ◽  
Activity Trackers ◽  
Free Living Conditions

BACKGROUND Wrist worn consumer-grade activity trackers are popular devices, developed mainly for personal use, but with the potential to be used also for clinical and research purposes. OBJECTIVE The objective of this study was to explore the validity, reliability and sensitivity to change of movement behaviours metrics from three popular activity trackers (POLAR Vantage M, Garmin Vivosport and Garmin Vivoactive 4s) in controlled and free-living conditions when worn by older adults. METHODS Participants (n = 28; 74 ± 5 years) underwent a videotaped laboratory protocol while wearing all three activity trackers. On a separate occasion, participants wore one (randomly assigned) activity tracker and a research grade physical activity monitor ActiGraph wGT3X-BT simultaneously for six consecutive days for comparisons. RESULTS Both Garmin activity trackers showed excellent performance for step counts, with mean absolute percentage error (MAPE) below 20 % and intraclass correlation coefficient (ICC2,1) above 0.90 (P < .05), while Polar Vantage M substantially over counted steps (MAPE = 84 % and ICC2,1 = 0.37 for free-living conditions). MAPE for sleep time was within 10 % for all the trackers tested, while far beyond 20 % for all the physical activity and calories burned outputs. Both Garmin trackers showed fair agreement (ICC2,1 = 0.58–0.55) for measuring calories burned when compared with ActiGraph. CONCLUSIONS Garmin Vivoactive 4s showed overall best performance, especially for measuring steps and sleep time in healthy older adults. Minimal detectible change was consistently lower for an average day measures than for a single day measure, but still relatively high. The results provided in this study could be used to guide choice on activity trackers aiming for different purposes – individual use/care, longitudinal monitoring or in clinical trial setting.

Physical Activity Monitor Accuracy for Overground Walking and Free-Living Conditions Among Pregnant Women

2020 ◽  
Vol 3 (2) ◽  
pp. 100-109
Author(s):  
Christopher P. Connolly ◽  
Jordana Dahmen ◽  
Robert D. Catena ◽  
Nigel Campbell ◽  
Alexander H.K. Montoye
Keyword(s):  
Physical Activity ◽  
Pregnant Women ◽  
Living Conditions ◽  
Overground Walking ◽  
Third Trimester ◽  
Free Living ◽  
Activity Monitors ◽  
Step Counting ◽  
Physical Activity Monitors ◽  
Free Living Conditions

Purpose: We aimed to determine the step-count validity of commonly used physical activity monitors for pregnancy overground walking and during free-living conditions. Methods: Participants (n = 39, 12–38 weeks gestational age) completed six 100-step overground walking trials (three self-selected “normal pace”, three “brisk pace”) while wearing five physical activity monitors: Omron HJ-720 (OM), New Lifestyles 2000 (NL), Fitbit Flex (FF), ActiGraph Link (AG), and Modus StepWatch (SW). For each walking trial, monitor-recorded steps and criterion-measured steps were assessed. Participants also wore all activity monitors for an extended free-living period (72 hours), with the SW used as the criterion device. Mean absolute percent error (MAPE) was calculated for overground walking and free-living protocols and compared across monitors. Results: For overground walking, the OM, NL, and SW performed well (<5% MAPE) for normal and brisk pace walking trials, and also when trials were analyzed by actual speeds. The AG and FF had significantly greater MAPE for overground walking trials (11.9–14.7%). Trimester did affect device accuracy to some degree for the AG, FF, and SW, with error being lower in the third trimester compared to the second. For the free-living period, the OM, NL, AG, and FF significantly underestimated (>32% MAPE) actual steps taken per day as measured by the criterion SW (M [SD] = 9,350 [3,910]). MAPE for the OM was particularly high (45.3%). Conclusion: The OM, NL, and SW monitors are valid measures for overground step-counting during pregnancy walking. However, the OM and NL significantly underestimate steps by second and third trimester pregnant women in free-living conditions.

scholarly journals Validity, Reliability and Sensitivity to Change of Three Consumer-Grade Activity Trackers in Controlled and Free-Living Conditions among Older Adults

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6245
Author(s):  
Kaja Kastelic ◽  
Marina Dobnik ◽  
Stefan Löfler ◽  
Christian Hofer ◽  
Nejc Šarabon
Keyword(s):  
Older Adults ◽  
Intraclass Correlation ◽  
Sleep Time ◽  
Living Conditions ◽  
Percentage Error ◽  
Sensitivity To Change ◽  
Free Living ◽  
Step Counts ◽  
Activity Trackers ◽  
Free Living Conditions

Wrist-worn consumer-grade activity trackers are popular devices, developed mainly for personal use. This study aimed to explore the validity, reliability and sensitivity to change of movement behaviors metrics from three activity trackers (Polar Vantage M, Garmin Vivoactive 4s and Garmin Vivosport) in controlled and free-living conditions when worn by older adults. Participants (n = 28; 74 ± 5 years) underwent a videotaped laboratory protocol while wearing all three trackers. On a separate occasion, participants (n = 17 for each of the trackers) wore one (randomly assigned) tracker and a research-grade activity monitor ActiGraph wGT3X-BT simultaneously for six consecutive days. Both Garmin trackers showed excellent performance for step counts, with a mean absolute percentage error (MAPE) below 20% and intraclass correlation coefficient (ICC2,1) above 0.90 (p < 0.05). The MAPE for sleep time was within 10% for all the trackers tested, while it was far beyond 20% for all other movement behaviors metrics. The results suggested that all three trackers could be used for measuring sleep time with a high level of accuracy, and both Garmin trackers could also be used for step counts. All other output metrics should be used with caution. The results provided in this study could be used to guide choice on activity trackers aiming for different purposes—individual use, longitudinal monitoring or in clinical trial setting.

scholarly journals Optimization and Validation of an Adjustable Activity Classification Algorithm for Assessment of Physical Behavior in Elderly

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5344
Author(s):  
Wouter Bijnens ◽  
Jos Aarts ◽  
An Stevens ◽  
Darcy Ummels ◽  
Kenneth Meijer
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Selection Process ◽  
Classification Algorithm ◽  
Living Conditions ◽  
Percentage Error ◽  
Activity Classification ◽  
Free Living ◽  
Algorithm Performance ◽  
Free Living Conditions

Due to a lack of transparency in both algorithm and validation methodology, it is difficult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (±10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.

Validity of Activity Monitor Step Detection Is Related to Movement Patterns

2016 ◽  
Vol 13 (2) ◽  
pp. 145-153 ◽  
Author(s):  
Amanda Hickey ◽  
Dinesh John ◽  
Jeffer E. Sasaki ◽  
Marianna Mavilia ◽  
Patty Freedson
Keyword(s):  
Low Frequency ◽  
Activity Monitor ◽  
Living Environment ◽  
Natural Setting ◽  
Rhythmic Movements ◽  
Free Living ◽  
Activity Monitors ◽  
Different Types ◽  
Step Counting ◽  
Free Living Conditions

Background:There is a need to examine step-counting accuracy of activity monitors during different types of movements. The purpose of this study was to compare activity monitor and manually counted steps during treadmill and simulated free-living activities and to compare the activity monitor steps to the StepWatch (SW) in a natural setting.Methods:Fifteen participants performed laboratory-based treadmill (2.4, 4.8, 7.2 and 9.7 km/h) and simulated free-living activities (eg, cleaning room) while wearing an activPAL, Omron HJ720-ITC, Yamax Digi-Walker SW-200, 2 ActiGraph GT3Xs (1 in “low-frequency extension” [AGLFE] and 1 in “normal-frequency” mode), an ActiGraph 7164, and a SW. Participants also wore monitors for 1-day in their free-living environment. Linear mixed models identified differences between activity monitor steps and the criterion in the laboratory/free-living settings.Results:Most monitors performed poorly during treadmill walking at 2.4 km/h. Cleaning a room had the largest errors of all simulated free-living activities. The accuracy was highest for forward/rhythmic movements for all monitors. In the free-living environment, the AGLFE had the largest discrepancy with the SW.Conclusion:This study highlights the need to verify step-counting accuracy of activity monitors with activities that include different movement types/directions. This is important to understand the origin of errors in step-counting during free-living conditions.

Safety and Performance of the Omnipod®Hybrid Closed-Loop System in Adolescents with Type 1 Diabetes over Five Days Under Free-Living Conditions

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1376-P
Author(s):  
GREGORY P. FORLENZA ◽  
BRUCE BUCKINGHAM ◽  
JENNIFER SHERR ◽  
THOMAS A. PEYSER ◽  
JOON BOK LEE ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Closed Loop ◽  
Living Conditions ◽  
Loop System ◽  
Closed Loop System ◽  
Free Living ◽  
And Performance ◽  
Free Living Conditions

Safety and Performance of the Omnipod® Hybrid Closed-Loop System in Adults with Type 1 Diabetes over Five Days Under Free-Living Conditions

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 207-OR
Author(s):  
BRUCE A. BUCKINGHAM ◽  
JENNIFER SHERR ◽  
GREGORY P. FORLENZA ◽  
THOMAS A. PEYSER ◽  
JOON BOK LEE ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Closed Loop ◽  
Living Conditions ◽  
Loop System ◽  
Closed Loop System ◽  
Free Living ◽  
And Performance ◽  
Free Living Conditions

scholarly journals Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jari Lipsanen ◽  
Liisa Kuula ◽  
Marko Elovainio ◽  
Timo Partonen ◽  
Anu-Katriina Pesonen
Keyword(s):  
Skin Surface ◽  
Living Conditions ◽  
Data Driven ◽  
Physiological Data ◽  
Free Living ◽  
Physiological Level ◽  
Temperature Rhythm ◽  
Time Series Modelling ◽  
Circadian Preference ◽  
Free Living Conditions

AbstractThe individual variation in the circadian rhythms at the physiological level is not well understood. Albeit self-reported circadian preference profiles have been consolidated, their premises are grounded on human experience, not on physiology. We used data-driven, unsupervised time series modelling to characterize distinct profiles of the circadian rhythm measured from skin surface temperature in free-living conditions. We demonstrate the existence of three distinct clusters of individuals which differed in their circadian temperature profiles. The cluster with the highest temperature amplitude and the lowest midline estimating statistic of rhythm, or rhythm-adjusted mean, had the most regular and early-timed sleep–wake rhythm, and was the least probable for those with a concurrent delayed sleep phase, or eveningness chronotype. While the clusters associated with the observed sleep and circadian preference patterns, the entirely unsupervised modelling of physiological data provides a novel basis for modelling and understanding the human circadian functions in free-living conditions.

scholarly journals Smartphone-Based Inertial Odometry for Blind Walkers

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4033
Author(s):  
Peng Ren ◽  
Fatemeh Elyasi ◽  
Roberto Manduchi
Keyword(s):  
Particle Filtering ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Mean Shift ◽  
Gait Pattern ◽  
Comparative Assessment ◽  
Pedestrian Tracking ◽  
Step Counter ◽  
Assisted Navigation

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

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