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Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers.

ABSTRACT: A number of important health-related outcomes are directly related to a person's ability to maintain normal gait speed. We hypothesize that cellular telephones may be repurposed to measure this important behavior in a noninvasive, continuous, precise, and inexpensive manner. The purpose of this study was to determine if physical activity (PA) counts collected by cell phone accelerometers could measure treadmill gait speeds. We also assessed how cell phone placement influenced treadmill gait speed measures. Participants included 55 young, middle-aged, and older community-dwelling men and women. We placed cell phones as a pendant around the neck, and on the left and right wrist, hip, and ankle. Subjects then completed an individualized treadmill protocol, alternating 1 min rest periods with 5 min of walking at different speeds (0.3-11.3 km/h; 0.2-7 mi/h). No persons were asked to walk at speeds faster than what they would achieve during day-to-day life. PA counts were calculated from all sensor locations. We built linear mixed statistical models of PA counts predicted by treadmill speeds ranging from 0.8 to 6.4 km/h (0.5-4 mi/h) while accounting for subject age, weight, and gender. We solved linear regression equations for treadmill gait speed, expressed as a function of PA counts, age, weight, and gender. At all locations, cell phone PA counts were strongly associated with treadmill gait speed. Cell phones worn at the hip yielded the best predictive model. We conclude that in both men and women, cell phone derived activity counts strongly correlate with treadmill gait speed over a wide range of subject ages and weights.

SUBMITTER: Carlson RH

PROVIDER: S-EPMC3387318 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers.

Carlson Richard H RH Huebner Derek R DR Hoarty Carrie A CA Whittington Jackie J Haynatzki Gleb G Balas Michele C MC Schenk Ana Katrin AK Goulding Evan H EH Potter Jane F JF Bonasera Stephen J SJ

Gait & posture 20120402 2

A number of important health-related outcomes are directly related to a person's ability to maintain normal gait speed. We hypothesize that cellular telephones may be repurposed to measure this important behavior in a noninvasive, continuous, precise, and inexpensive manner. The purpose of this study was to determine if physical activity (PA) counts collected by cell phone accelerometers could measure treadmill gait speeds. We also assessed how cell phone placement influenced treadmill gait spee ...[more]

PMID: 22475727

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Project description:BACKGROUND AND OBJECTIVES:Virtual reality (VR) is increasingly often finding applications in physiotherapy and health promotion. Recent years have seen the use of advanced technologies in the promotion of physical activity (PA) in society. New simulators, e.g., treadmills, enable the performance of PA (e.g., locomotive movements) in VR (artificially created virtual world). The question of how such movements are similar to natural forms of human locomotion (march, run) inspired the comparative analysis of physiological gait and gait in VR on a multidirectional Omni treadmill. MATERIALS AND METHODS:The tests involved the use of the BTS Smart system for the triplanar analysis of motion. The test involved 10 healthy females aged 20-24 (weight: 52 ± 3.1 kg, height 162 ± 5.4 cm). Measurements were performed at two stages. The first stage involved the standard assessment of physiological gait, whereas the second was focused on gait forced by the Omni treadmill. The following gait parameters were analyzed: Flexion-extension in the ankle, knee joint and hip joint, rotation in the hip joint and knee joint, foot progression, adduction-abduction in the knee joint and hip joint, pelvic obliquity, pelvic tilt, pelvic rotation as well as energy expenditure and the movement of the body center of mass. RESULTS:The analysis of the test results revealed the existence of differences in the kinematics of physical gait and gait on the treadmill. The greatest differences were recorded in relation to the dorsal-plantar flexion in the ankle, the foot progression, the rotation of the knee joint, pelvic tilt and rotation. In addition, the gait on the treadmill is characterized by the longer duration of the stance phase and reduced ranges of the following movements: Flexion-extension in the ankle, knee joint and hip joint, adduction-abduction in the hip joint as well as rotation in the ankle and hip joint. The values of potential, kinetic and total energy recorded in relation to forced gait are significantly lower than those of physiological gait. CONCLUSIONS:Because of the fact that the parameters of gait on the Omni platform vary significantly from the parameters of physical gait, the application of the Omni treadmill in the re-education of gait during rehabilitation should be treated with considerable care. Nonetheless, the treadmill has adequate potential to become a safe simulator enabling active motion in VR using locomotive movements.

Project description:BACKGROUND:Physical activity (PA) level is associated with multiple health benefits during early childhood. However, inconsistency in the methods for quantification of PA levels among preschoolers remains a problem. OBJECTIVE:This study aimed to develop PA intensity cut points for wrist-worn accelerometers by using machine learning (ML) approaches to assess PA in preschoolers. METHODS:Wrist- and hip-derived acceleration data were collected simultaneously from 34 preschoolers on 3 consecutive preschool days. Two supervised ML models, receiver operating characteristic curve (ROC) and ordinal logistic regression (OLR), and one unsupervised ML model, k-means cluster analysis, were applied to establish wrist-worn accelerometer vector magnitude (VM) cut points to classify accelerometer counts into sedentary behavior, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA). Physical activity intensity levels identified by hip-worn accelerometer VM cut points were used as reference to train the supervised ML models. Vector magnitude counts were classified by intensity based on three newly established wrist methods and the hip reference to examine classification accuracy. Daily estimates of PA were compared to the hip-reference criterion. RESULTS:In total, 3600 epochs with matched hip- and wrist-worn accelerometer VM counts were analyzed. All ML approaches performed differently on developing PA intensity cut points for wrist-worn accelerometers. Among the three ML models, k-means cluster analysis derived the following cut points: ?2556 counts per minute (cpm) for sedentary behavior, 2557-7064 cpm for LPA, 7065-14532 cpm for MPA, and ?14533 cpm for VPA; in addition, k-means cluster analysis had the highest classification accuracy, with more than 70% of the total epochs being classified into the correct PA categories, as examined by the hip reference. Additionally, k-means cut points exhibited the most accurate estimates on sedentary behavior, LPA, and VPA as the hip reference. None of the three wrist methods were able to accurately assess MPA. CONCLUSIONS:This study demonstrates the potential of ML approaches in establishing cut points for wrist-worn accelerometers to assess PA in preschoolers. However, the findings from this study warrant additional validation studies.

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Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers.

Publications

Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers.

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