In this work, algorithms are developed and evaluated to de- tect physical activities from data acquired using five small biaxial ac- celerometers worn simultaneously on different parts of the body. Ac- celeration data was collected from 20 subjects without researcher su- pervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. De- cision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers - thigh and wrist - the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves.

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Activity recognition from user-annotated acceleration data

Purpose: The purpose of this review is to address important methodological issues related to conducting accelerometer-based assessments of physical activity in free-living individuals. Methods: We review the extant scientific literature for empirical information related to the following issues: product selection, number of accelerometers needed, placement of accelerometers, epoch length, and days of monitoring required to estimate habitual physical activity. We also discuss the various options related to distributing and collecting monitors and strategies to enhance compliance with the monitoring protocol. Results: No definitive evidence exists currently to indicate that one make and model of accelerometer is more valid and reliable than another. Selection of accelerometer therefore remains primarily an issue of practicality, technical support, and comparability with other studies. Studies employing multiple accelerometers to estimate energy expenditure report only marginal improvements in explanatory power. Accelerometers are best placed on hip or the lower back. Although the issue of epoch length has not been studied in adults, the use of count cut points based on 1-min time intervals maybe inappropriate in children and may result in underestimation of physical activity. Among adults, 3-5 d of monitoring is required to reliably estimate habitual physical activity. Among children and adolescents, the number of monitoring days required ranges from 4 to 9 d, making it difficult to draw a definitive conclusion for this population. Face-to-face distribution and collection of accelerometers is probably the best option in field-based research, but delivery and return by express carrier or registered mail is a viable option. Conclusion: Accelerometer-based activity assessments requires careful planning and the use of appropriate strategies to increase compliance.

Conducting accelerometer-based activity assessments in field-based research.

The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence

Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring

OBJECTIVES: To re-state the principles underlying the Goldberg cut-off for identifying under-reporters of energy intake, re-examine the physiological principles and update the values to be substituted into the equation for calculating the cut-off, and to examine its use and limitations. RESULTS: New values are suggested for each element of the Goldberg equation. The physical activity level (PAL) for comparison with energy intake:basal metabolic rate (EI:BMR) should be selected to reflect the population under study; the PAL value of 1.55 x BMR is not necessarily the value of choice. The suggested value for average within-subject variation in energy intake is 23% (unchanged), but other sources of variation are increased in the light of new data. For within-subject variation in measured and estimated BMR, 4% and 8.5% respectively are suggested (previously 2.5% and 8%), and for total between-subject variation in PAL, the suggested value is 15% (previously 12.5%). The effect of these changes is to widen the confidence limits and reduce the sensitivity of the cut-off. CONCLUSIONS: The Goldberg cut-off can be used to evaluate the mean population bias in reported energy intake, but information on the activity or lifestyle of the population is needed to choose a suitable PAL energy requirement for comparison. Sensitivity for identifying under-reporters at the individual level is limited. In epidemiological studies information on home, leisure and occupational activity is essential in order to assign subjects to low, medium or high PAL levels before calculating the cut-offs. In small studies, it is desirable to measure energy expenditure, or to calculate individual energy requirements, and to compare energy intake directly with energy expenditure.

Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations

Background
Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring.

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A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation

The present study describes the development of a triaxial accelerometer (TA) and a portable data processing unit for the assessment of daily physical activity. The TA is composed of three orthogonally mounted uniaxial piezoresistive accelerometers and can be used to register accelerations covering the amplitude and frequency ranges of human body acceleration. Interinstrument and test-retest experiments showed that the offset and the sensitivity of the TA were equal for each measurement direction and remained constant on two measurement days. Transverse sensitivity was significantly different for each measurement direction, but did not influence accelerometer output (<3% of the sensitivity along the main axis). The data unit enables the on-line processing of accelerometer output to a reliable estimator of physical activity over eight-day periods. Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity (r=0.89). Shortcomings of the system are its low sensitivity to sedentary activities and the inability to register static exercise. The validity of the system for the assessment of normal daily physical activity and specific activities outside the laboratory should be studied in free-living subjects.

/pdf/a-triaxial-accelerometer-and-portable-data-processing-unit-5dbzik0f3x.pdf

A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity

The use of movement registration for daily physical activity assessment was evaluated during a 7-day period in 30 free-living subjects. Body movement was registered with a Tracmor motion sensor consisting of a triaxial accelerometer and a data unit for on-line processing of accelerometer output over 1-min intervals. Average Tracmor output was correlated against four different energy estimates: 1) average daily metabolic rate (ADMR), determined with doubly labeled water; 2) ADMR-sleeping metabolic rate (SMR; determined in a respiration chamber); 3) (ADMR-SMR) per kilogram of body mass; and 4) the overall physical activity level (PAL = ADMR/SMR). The highest correlation was found for the relationship between Tracmor output and PAL (r = 0.58). After correction for Tracmor values arising from vibrations produced by transportation means, this correlation was improved to 0.73. There was no difference between Tracmor output and PAL in discriminating between overall activity levels with "low" (PAL 1.85) intensity. It is concluded that the Tracmor can be used in free-living subjects to distinguish among interindividual as well as intraindividual levels of daily physical activity.

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Daily physical activity assessment: comparison between movement registration and doubly labeled water

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Effects of placement and orientation of body-fixed accelerometers on the assessment of energy expenditure during walking

/pdf/evaluation-of-a-triaxial-accelerometer-for-the-assessment-of-3x2f3oh5kc.pdf

M Verduin

Papers

A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity

Daily physical activity assessment: comparison between movement registration and doubly labeled water

Effects of placement and orientation of body-fixed accelerometers on the assessment of energy expenditure during walking

Evaluation of a triaxial accelerometer for the assessment of daily physical activity