Occupant motion sensors: rotational accelerometer development. Technical report.

Mode of access: Internet.

Two-axis temperature-insensitive accelerometer based on multicore fiber Bragg gratings

We report a compact two-dimensional accelerometer based upon a simple fiber cantilever constructed from a short length of multicore optical fiber. Two-axis measurement is demonstrated up to 3 kHz. Differential measurement between fiber Bragg gratings written in the multicore fiber provides temperature- insensitive measurements.

Two-axis accelerometer based on multicore fibre Bragg gratings

We report an accelerometer based upon a simple fibre cantilever constructed from a short length of multicore fibre(MCF) containing fibre Bragg gratings (FBGs). Two-axis measurement is demonstrated up to 3 kHz.

Two-axis accelerometer based on multicore fibre Bragg gratings

We report an accelerometer based upon a simple fibre cantilever constructed from a short length of multicore fibre(MCF) containing fibre Bragg gratings (FBGs). Two-axis measurement is demonstrated up to 3 kHz.

Imputing accelerometer non-wear time when assessing children's moderate-to-vigorous physical activity

BACKGROUND: Moderate-to-vigorous physical activity (MVPA) is an important determinant of children’s physical health, and is commonly measured using accelerometers. A major limitation of accelerometers is non-wear time, which is the time the participant did not wear their device. Given that non-wear time is traditionally discarded from the dataset prior to estimating MVPA, final estimates of MVPA may be biased. Therefore, alternate approaches should be explored. OBJECTIVES: The objectives of this thesis were to 1) develop and describe an imputation approach that uses the socio-demographic, time, health, and behavioural data from participants to replace non-wear time accelerometer data, 2) determine the extent to which imputation of non-wear time data influences estimates of MVPA, and 3) determine if imputation of non-wear time data influences the associations between MVPA, body mass index (BMI), and systolic blood pressure (SBP). METHODS: Seven days of accelerometer data were collected using Actical accelerometers from 332 children aged 10-13. Three methods for handling missing accelerometer data were compared: 1) the “non-imputed” method wherein non-wear time was deleted from the dataset, 2) imputation dataset I, wherein the imputation of MVPA during non-wear time was based upon socio-demographic factors of the participant (e.g., age), health information (e.g., BMI), and time characteristics of the non-wear period (e.g., season), and 3) imputation dataset II wherein the imputation of MVPA was based upon the same variables as imputation dataset I, plus organized sport information. Associations between MVPA and health outcomes in each method were assessed using linear regression. RESULTS: Non-wear time accounted for 7.5% of epochs during waking hours. The average minutes/day of MVPA was 56.8 (95% CI: 54.2, 59.5) in the non-imputed dataset, 58.4 (95% CI: 55.8, 61.0) in imputed dataset I, and 59.0 (95% CI: 56.3, 61.5) in imputed dataset II. Estimates between datasets were not significantly different. The strength of the relationship between MVPA with BMI and SBP were comparable between all three datasets. CONCLUSION: These findings suggest that studies that achieve high accelerometer compliance with unsystematic patterns of missing data can use the traditional approach of deleting non-wear time from the dataset to obtain MVPA measures without substantial bias.

Measurement properties of the Australian Women's Activity Survey

Purpose: The Australian Women’s Activity Survey (AWAS) was developed based on a systematic review and qualitative research on how to measure activity patterns of women with young children (WYC). AWAS assesses activity performed across five domains (planned activities, employment, childcare, domestic responsibilities and transport), and intensity levels (sitting, light-intensity, brisk walking, moderate-intensity and vigorous-intensity) in a typical week in the past month. The purpose of this study was to assess the test-retest reliability and criterion validity of the AWAS. Methods: WYC completed the AWAS on two occasions 7-d apart (test-retest reliability protocol) and/or wore an MTI ActiGraph accelerometer for 7-d in between (validity protocol). Forty WYC (mean age 35 ± 5yrs) completed the test-retest reliability protocol and 75 WYC (mean age 33 ± 5yrs) completed the validity protocol. Interclass Correlation Coefficients (ICC) between AWAS administrations and Spearman’s Correlation Coefficients (rs) between AWAS and MTI data were calculated. Results: AWAS showed good test-retest reliability (ICC=0.80 (0.65-0.89)) and acceptable criterion validity (rs= 0.28, p=0.01) for measuring weekly health-enhancing physical activity. AWAS also provided repeatable and valid estimates of sitting time (test-retest reliability ICC=0.42 (0.13-0.64), and criterion validity (rs= 0.32, p=0.006)). Conclusion: The measurement properties of the AWAS are comparable to those reported for existing self-report measures of physical activity. However, AWAS offers a more comprehensive and flexible alternative for accurately assessing different domains and intensities of activity relevant to WYC. Future research should investigate whether the AWAS is a suitable measure of intervention efficacy by examining its sensitivity to change.

Exercise intensity influences NEAT in overweight and obese adults

PURPOSE: To determine the effect of acute bouts of moderate- and high-intensity walking exercise on non-exercise activity thermogenesis (NEAT) in overweight and obese adults. ---------- METHOD: 16 participants performed a single bout of either moderate-intensity walking exercise (MIE) or high-intensity walking exercise (HIE) on two separate occasions. The MIE consisted of walking for 60 minutes on a motorized treadmill at 6 km.h. The 60-minute HIE session consisted of walking in 5-min intervals at 6 km.h and 10% grade followed by 5-min at 0% grade. NEAT was assessed by accelerometer on three days before, the day of, and three days following the exercise sessions. ---------- RESULTS: There was no significant difference in NEAT vector magnitude (counts.min) between the pre-exercise period (days 1-3) and the exercise day (day 4) for either MIE or HIE protocol. In addition, there was no change in NEAT during the three days following the MIE session, however NEAT increased by 16% on day 7 (post-exercise) compared with exercise day (P = 0.32). However during the post-exercise period following the HIE session, NEAT was increased by 25% on day 7 compared with the exercise day (P = 0.08), and by 30-33% compared with pre-exercise period (day 1, day 2 and day 3); P = 0.03, 0.03, 0.02, respectively. ---------- CONCLUSION: A single bout of either MIE or HIE did not alter NEAT on the exercise day or on the first two days following the exercise session. However, monitoring NEAT on a third day allowed the detection of a 48-h delay in increased NEAT after performing HIE. A longer-term intervention is needed to determine the effect of accumulated exercise sessions over a week on NEAT.

Response to "a step in the right direction : commentary on expected values for pedometer-determined physical activity in youth"

The figure Beets took exception to displays sex‐ and age‐specific median values of aggregated published expected values for pedometer determined physical activity.

Expected values for pedometer-determined physical activity in older populations

The purpose of this review is to update expected values for pedometer-determined physical activity in free-living healthy older populations. A search of the literature published since 2001 began with a keyword (pedometer, "step counter," "step activity monitor" or "accelerometer AND steps/day") search of PubMed, Cumulative Index to Nursing & Allied Health Literature (CINAHL), SportDiscus, and PsychInfo. An iterative process was then undertaken to abstract and verify studies of pedometer-determined physical activity (captured in terms of steps taken; distance only was not accepted) in free-living adult populations described as ≥ 50 years of age (studies that included samples which spanned this threshold were not included unless they provided at least some appropriately age-stratified data) and not specifically recruited based on any chronic disease or disability. We identified 28 studies representing at least 1,343 males and 3,098 females ranging in age from 50–94 years. Eighteen (or 64%) of the studies clearly identified using a Yamax pedometer model. Monitoring frames ranged from 3 days to 1 year; the modal length of time was 7 days (17 studies, or 61%). Mean pedometer-determined physical activity ranged from 2,015 steps/day to 8,938 steps/day. In those studies reporting such data, consistent patterns emerged: males generally took more steps/day than similarly aged females, steps/day decreased across study-specific age groupings, and BMI-defined normal weight individuals took more steps/day than overweight/obese older adults. The range of 2,000–9,000 steps/day likely reflects the true variability of physical activity behaviors in older populations. More explicit patterns, for example sex- and age-specific relationships, remain to be informed by future research endeavors.

Detecting gesture force peaks for intuitive interaction

With the release of the Nintendo Wii in 2006, the use of haptic force gestures has become a very popular form of input for interactive entertainment. However, current gesture recognition techniques utilised in Nintendo Wii games fall prey to a lack of control when it comes to recognising simple gestures. This paper presents a simple gesture recognition technique called Peak Testing which gives greater control over gesture interaction. This recognition technique locates force peaks in continuous force data (provided by a gesture device such as the Wiimote) and then cancels any peaks which are not meant for input. Peak Testing is therefore technically able to identify movements in any direction. This paper applies this recognition technique to control virtual instruments and investigates how users respond to this interaction. The technique is then explored as the basis for a robust way to navigate menus with a simple flick of the wrist. We propose that this flick-form of interaction could be a very intuitive way to navigate Nintendo Wii menus instead of the current pointer techniques implemented.

Simple classification of walking activities using commodity smart phones

People interact with mobile computing devices everywhere, while sitting, walking, running or even driving. Adapting the interface to suit these contexts is important, thus this paper proposes a simple human activity classification system. Our approach uses a vector magnitude recognition technique to detect and classify when a person is stationary (or not walking), casually walking, or jogging, without any prior training. The user study has confirmed the accuracy.