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Real-time monitoring of human physical activity (PA) is important for assessing the intensity of activity and exposure to environmental pollutions. A wireless wearable multisenor integrated measurement system (WIMS) has been designed for real-time measurement of the energy expenditure and breathing volume of human subjects under free-living conditions. To address challenges posted by the limited battery life and data synchronization requirement among multiple sensors in the system, the ZigBee communication platform has been explored for energy-efficient design. Two algorithms have been developed (multiData packaging and slot-data-synchronization) and coded into a microcontroller (MCU)-based sensor circuitry for real-time control of wireless data communication. Experiments have shown that the design enables continued operation of the wearable system for up to 68 h, with the maximum error for data synchronization among the various sensor nodes (SNs) being less than 24 ms. Experiment under free-living conditions have shown that the WIMS is able to correctly recognize the activity intensity level 86% of the time. The results demonstrate the effectiveness of the energy-efficient wireless design for human PA monitoring. [ABSTRACT FROM PUBLISHER]

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multisensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which activity type and energy expenditure are derived. The results show that the method correctly recognized the 13 activity types 88.1% of the time, which is 12.3% higher than using a hip accelerometer alone. Also, the method predicted energy expenditure with a root mean square error of 0.42 METs, 22.2% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor were added to the fusion model. These results demonstrate that the multisensor fusion technique presented is more effective in identifying activity type and energy expenditure than the traditional accelerometer-alone-based methods. [ABSTRACT FROM PUBLISHER]

Purpose: To assess the convergent validity of body worn wearable camera (WC) still-images (IMGs) for determining posture compared with activPAL (AP) classifications., Methods: Participants (n=16, mean age 46.7±23.8yrs, 9F) wore an Autographer WC above the xyphoid process and an AP during three, 2hr free-living visits. IMGs were captured on average 8.47 seconds apart and were annotated with output consisting of events, transitory states, unknown and gaps. Events were annotations that matched AP classifications (sit, stand and move) consisting of at least 3 IMGs, transitory states were posture annotations fewer than 3 IMGs, unknown were IMGs that could not be accurately classified, and gaps were time between annotations. For analyses, annotation and AP output were converted to one-sec epochs and matched second-by-second. Total and average length of visits and events are reported in minutes. Bias and 95% CIs for event posture times from IMGs to AP posture times were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification., Results: 43 visits were analyzed with a total visit and event time of 5027.73 and 4237.23 minutes and average visit and event lengths being 116.92 and 98.54 minutes, respectively. Bias was not statistically significant for sitting but significant for standing and movement (0.84, -6.87 and 6.04 minutes). From confusion matrices, IMGs correctly classified sitting, standing and movement 85.69%, 54.87%, and 69.41% of total AP time, respectively., Conclusion: WC IMGs provide a good estimation of overall sitting time but underestimate standing and overestimate movement time. Future work is warranted to improve posture classifications and examine IMG accuracy and precision in assessing activity type behaviors.

This review proposes that the emerging acceptance of the hormetic dose-response model in toxicology and pharmacology has the potential to significantly change important aspects of drug development. Two situations where the hormesis concept may affect drug development are considered: one in which low-dose stimulation may represent an adverse/unwanted effect (eg, stimulation of tumor cell proliferation by antitumor drugs), the other in which low-dose stimulation defines the therapeutic zone (ie, a beneficial or intended effect; eg, cognition enhancement in Alzheimer's disease treatment). Examples are used to demonstrate that the hormetic dose-response model has implications for the definition of an ideal candidate for a therapeutic agent, as well as implications for study designs needed to assess the quantitative features of the dose-response relationship.