Ready, Set, Move! Tracking Children's Modified Ride-On Car Use With a Custom Data Logger.

PURPOSE: To create and implement a next-generation, custom data logger to automatically track modified ride-on car (MROC) use in home and community settings, establish feasibility of long-term remote collection of community MROC use data, describe trends of MROC use, and explore parent perception of the MROC. METHODS: In this descriptive study, a custom data logger was constructed and integrated into MROCs using an Arduino Pro-Mini microprocessor to capture real-time use data remotely. RESULTS: It is feasible to automatically track MROC use in home and community settings. On average, MROC use trends appear consistent with caregiver reports and show higher initial use with steadily decreasing frequency over time, and varying bout duration and play session length, despite favorable caregiver perceptions of the cars. CONCLUSIONS: Remote tracking of MROC use may decrease burden on busy families and provide clinicians with valuable technology use data.

Off to the park: a geospatial investigation of adapted ride-on car usage.

PURPOSE: Adapted ride-on cars (ROC) are an affordable, power mobility training tool for young children with disabilities. Previous qualitative research has identified environmental factors, such as weather and adequate drive space, as barriers to families' adoption of their ROC. However, we do not currently know the relationship between the built environment and ROC usage. MATERIALS AND METHODS: In our current study, we quantified the driving patterns of 14 children (2.5 ± 1.45 years old, 8 male: 6 female) using ROCs outside and inside of their homes over the course of a year using a custom datalogger and geospatial data. To measure environmental accessibility, we used the AccessScore from Project Sidewalk, an open-source accessibility mapping initiative, and the Walk Score, a measure of neighborhood pedestrian-friendliness. RESULTS: The number of play sessions with the ROC ranged from 1 to 76; 4 participants used it less than 10 times and 4 participants used it more than 50 times. Our findings indicate that more play sessions took place indoors, within the participants' homes. However, when the ROC was used outside the home, children engaged in longer play sessions, actively drove for a larger portion of the session, and covered greater distances. Most children tended to drive their ROCs in close proximity to their homes, with an average maximum distance from home of 181 meters. Most notably, we found that children drove more in pedestrian-friendly neighborhoods and when in proximity to accessible paths. CONCLUSIONS: The accessibility of the built environment is paramount when providing any form of mobility device to a child. Providing an accessible place for a child to move, play, and explore is critical in helping a child and family adopt the mobility device into their daily life.

IMPLICATIONS FOR REHABILITATION: GPS OF ROC USAGERide-on cars provided a novel means for young children with disabilities to explore their home and community environments.Children drove their adapted ride-on cars for longer periods of time outside than inside, and in close proximity to their homes.The identification of an accessible route increased driving frequency and drive session duration. Recommending accessible routes and play locations where families can use their adapted ride-on car may be an important aspect of increasing mobility technology use.Because there were a higher number of play sessions inside, it is important to consider indoor accessibility when designing and implementing mobility technology for young children.

Modeling Clinically Validated Physical Activity Assessments Using Commodity Hardware.

Consumer-grade wearable activity devices such as Fitbits are increasingly being used in research settings to promote physical activity (PA) due to their low-cost and widespread popularity. However, Fitbit-derived measures of activity intensity are consistently reported to be less accurate than intensity estimates obtained from research-grade accelerometers (i.e., ActiGraph). As such, the potential for using a Fitbit to measure PA intensity within research contexts remains limited. This study aims to model ActiGraph-based intensity estimates from the validated Freedson vector magnitude (VM3) algorithm using measures of steps, metabolic equivalents, and intensity levels obtained from Fitbit. Minute-level data collected from 19 subjects, who concurrently wore the ActiGraph GT3X and Fitbit Flex devices for an average of 1.8 weeks, were used to generate the model. After testing several modeling methods, a naïve Bayes classifier was chosen based on the lowest achieved error rate. Overall, the model reduced Fitbit to ActiGraph errors from 19.97% to 16.32%. Moreover, the model reduced misclassification of Fitbit-based estimates of moderate-to-vigorous physical activity (MVPA) by 40%, eliminating a statistically significant difference between MVPA estimates derived from ActiGraph and Fitbit. Study findings support the general utility of the model for measuring MVPA with the Fitbit Flex in place of the more costly ActiGraph GT3X accelerometer for young healthy adults.

Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions.

BACKGROUND: Wearable activity monitors such as Fitbit enable users to track various attributes of their physical activity (PA) over time and have the potential to be used in research to promote and measure PA behavior. However, the measurement accuracy of Fitbit in absolute free-living conditions is largely unknown. OBJECTIVE: To examine the measurement congruence between Fitbit Flex and ActiGraph GT3X for quantifying steps, metabolic equivalent tasks (METs), and proportion of time in sedentary activity and light-, moderate-, and vigorous-intensity PA in healthy adults in free-living conditions. METHODS: A convenience sample of 19 participants (4 men and 15 women), aged 18-37 years, concurrently wore the Fitbit Flex (wrist) and ActiGraph GT3X (waist) for 1- or 2-week observation periods (n=3 and n=16, respectively) that included self-reported bouts of daily exercise. Data were examined for daily activity, averaged over 14 days and for minutes of reported exercise. Average day-level data included steps, METs, and proportion of time in different intensity levels. Minute-level data included steps, METs, and mean intensity score (0 = sedentary, 3 = vigorous) for overall reported exercise bouts (N=120) and by exercise type (walking, n=16; run or sports, n=44; cardio machine, n=20). RESULTS: Measures of steps were similar between devices for average day- and minute-level observations (all P values > .05). Fitbit significantly overestimated METs for average daily activity, for overall minutes of reported exercise bouts, and for walking and run or sports exercises (mean difference 0.70, 1.80, 3.16, and 2.00 METs, respectively; all P values < .001). For average daily activity, Fitbit significantly underestimated the proportion of time in sedentary and light intensity by 20% and 34%, respectively, and overestimated time by 3% in both moderate and vigorous intensity (all P values < .001). Mean intensity scores were not different for overall minutes of exercise or for run or sports and cardio-machine exercises (all P values > .05). CONCLUSIONS: Fitbit Flex provides accurate measures of steps for daily activity and minutes of reported exercise, regardless of exercise type. Although the proportion of time in different intensity levels varied between devices, examining the mean intensity score for minute-level bouts across different exercise types enabled interdevice comparisons that revealed similar measures of exercise intensity. Fitbit Flex is shown to have measurement limitations that may affect its potential utility and validity for measuring PA attributes in free-living conditions.

Robust Automated Step Extraction From Time-Series Contact Force Data Using the PDShoe.

This paper presents a method of stride identification, extraction, and analysis of data sets of time-series contact force data for ambulating subjects both with and without Parkinson's disease (PD). This method has been made robust with the use of seeded K-Means clustering, fast Fourier transformation (FFT) spectral analysis, and minimum window size rejection. These methods combine to produce well selected strides of active walking data. We are able to calculate quality of walking measures of stride duration, stance duration (as percent of gait cycle - %GC), swing duration (%GC), time to maximum heel force (%GC), time to maximum toe force (%GC), time spent in heel contact (%GC), and time spent in toe contact (%GC).

The effect of step-synchronized vibration on patients with Parkinson's disease: case studies on subjects with freezing of gait or an implanted deep brain stimulator.

Identifying noninvasive treatments to alleviate the symptoms of Parkinson's disease (PD) is important to improving the quality of life for those with PD. Several studies have explored the effects of visual, auditory, and vibratory cueing to improve gait in PD patients. Here, we present a wireless vibratory feedback system, called the PDShoe, and an associated intervention study with four subjects. The PDShoe was used on two control subjects, one subject with PD who experienced freezing of gait (FOG), and one subject with PD with an implanted deep brain stimulator (DBS). This short intervention study showed statistically significant improvements in peak heel pressure timing, peak toe pressure timing, time on the heel sensor, and stance to swing ratio after just one week of twice-daily therapy. Thus, step-synchronized vibration applied to the feet of patients with PD may be an effective way to improve gait in those subjects.

An untethered shoe with vibratory feedback for improving gait of Parkinson's patients: the PDShoe.

Subjects with Parkinson's disease (PD) often have trouble with ambulation. Some research has shown that auxiliary cueing in the form of vision, audio, or vibration can improve the gait of PD patients. We have developed a new vibratory feedback shoe, known as the PDShoe, which builds on existing research. This device can modulate both frequency and amplitude of feedback for the wearer. It is untethered, and thus can be worn during daily activities. Pressure and tactor status data are transmitted wirelessly over a personal area network to a notebook computer. This computer can also control the tactor actuation and stimulation frequency. This paper describes the details of design and construction of the PDShoe. A preliminary evaluation with four Parkinson's disease subjects and two healthy subjects is included to show the usability of the device.

Design of a minimally constraining, passively supported gait training exoskeleton: ALEX II.

This paper discusses the design of a new, minimally constraining, passively supported gait training exoskeleton known as ALEX II. This device builds on the success and extends the features of the ALEX I device developed at the University of Delaware. Both ALEX (Active Leg EXoskeleton) devices have been designed to supply a controllable torque to a subject's hip and knee joint. The current control strategy makes use of an assist-as-needed algorithm. Following a brief review of previous work motivating this redesign, we discuss the key mechanical features of the new ALEX device. A short investigation was conducted to evaluate the effectiveness of the control strategy and impact of the exoskeleton on the gait of six healthy subjects. This paper concludes with a comparison between the subjects' gait both in and out of the exoskeleton.