Design and Validation of a Radio-Frequency Identification-Based Device for Routinely Assessing Gait Speed in a Geriatrics Clinic.

OBJECTIVES: To evaluate the feasibility, acceptability, and validity of a radio-frequency identification (RFID)-based system to measure gait speed in a clinical setting as a first step to using unobtrusive gait speed assessment in routine clinical care. DESIGN: Feasibility study comparing gait speed assessed using an RFID-based system with gait speed assessed using handheld stopwatch, the criterion standard. SETTING: Outpatient geriatrics clinic at a Connecticut-based academic medical center. PARTICIPANTS: Clinic attendees who could walk independently with or without an assistive device (N=50) and healthcare providers (N=9). MEASUREMENTS: Gait speed was measured in twice using 2 methods each time before participants entered an examination room. Participants walked at their usual pace while gait speed was recorded simultaneously using the RFID-based system and a handheld stopwatch operated by a trained study investigator. After 2 trials, participants completed a brief survey regarding their experience. At the end of the study period, clinic healthcare providers completed a separate survey. RESULTS: Test-retest reliability of the RFID-based system was high (intraclass correlation coefficient = 0.953). The mean difference ± standard deviation in gait speed between the RFID-based system and the stopwatch was -0.003±0.035 m/s (p=.53) and did not differ significantly according to age, sex, or use of an assistive walking aid. Acceptability of the device was high, and 8 of 9 providers indicated that measuring gait speed using the RFID-based system should be a part of routine clinical care. CONCLUSION: RFID technology may offer a practical means of overcoming barriers to routine measurement of gait speed in real-world outpatient clinical settings.

A Novel Hybrid Error Criterion-Based Active Control Method for on-Line Milling Vibration Suppression with Piezoelectric Actuators and Sensors.

Milling vibration is one of the most serious factors affecting machining quality and precision. In this paper a novel hybrid error criterion-based frequency-domain LMS active control method is constructed and used for vibration suppression of milling processes by piezoelectric actuators and sensors, in which only one Fast Fourier Transform (FFT) is used and no Inverse Fast Fourier Transform (IFFT) is involved. The correction formulas are derived by a steepest descent procedure and the control parameters are analyzed and optimized. Then, a novel hybrid error criterion is constructed to improve the adaptability, reliability and anti-interference ability of the constructed control algorithm. Finally, based on piezoelectric actuators and acceleration sensors, a simulation of a spindle and a milling process experiment are presented to verify the proposed method. Besides, a protection program is added in the control flow to enhance the reliability of the control method in applications. The simulation and experiment results indicate that the proposed method is an effective and reliable way for on-line vibration suppression, and the machining quality can be obviously improved.

Tissue artifact removal from respiratory signals based on empirical mode decomposition.

On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.

Wireless design of a multisensor system for physical activity monitoring.

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.

Computational methods for estimating energy expenditure in human physical activities.

Accurate and reliable methods for assessing human physical activity (PA) energy expenditure (PAEE) are informative and essential for understanding individual behaviors and quantifying the effect of PA on disease, for PA surveillance, and for examining determinants of PA in different populations. This article reviews recent advances in the estimation of PAEE in three interrelated areas: 1) types of sensors worn by human subjects, 2) features extracted from the measured sensor signals, and 3) modeling techniques to estimate the PAEE using these features. The review illustrates three directions in the PAEE studies and provides recommendations for future research, with the aim to produce valid, reliable, and accurate assessment of PAEE from wearable sensors.

Multisensor data fusion for physical activity assessment.

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.

Classification of physical activities based on sparse representation.

This paper presents a new classification method for physical activity assessment, based on sparse representation. This method bypasses the need for feature extraction and selection that is typically involved for activity classification, and classifies activities using raw sensor signals directly. Higher discriminative power than that from the conventional k-nearest neighbor algorithm has been demonstrated through experiments performed on 105 subjects.

ZigBee-based wireless multi-sensor system for physical activity assessment.

Physical activity (PA) is important for assessing human exposure to the environment. This paper presents a ZigBee-based Wireless wearable multi-sensor Integrated Measurement System (WIMS) for in-situ PA measurement. Two accelerometers, a piezoelectric displacement sensor, and an ultraviolet (UV) sensor have been used for the physical activity assessment. Detailed analysis was performed for the hardware design and embedded program control, enabling efficient data sampling and transmission, compact design, and extended battery life to meet requirements for PA assessment under free-living conditions. Preliminary testing of the WIMS has demonstrated the functionality of the design, while performance comparison of the WIMS with a wired version on an electromagnetic shaker has demonstrated the signal validity.

SVM-based multi-sensor fusion for free-living physical activity assessment.

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on the support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multi-sensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which the activity types and related energy expenditures are derived. The result shows that the method correctly recognized the 13 activity types 84.7% of the time, which is 26% higher than using a hip accelerometer alone. Also, the method predicted the associated energy expenditure with a root mean square error of 0.43 METs, 43% 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 was added to the fusion model. These results demonstrate that the multi-sensor fusion technique presented is more effective in assessing activities of varying intensities than the traditional accelerometer-alone based methods.

Midpoint-based empirical decomposition for nonlinear trend estimation.

This paper presents a new method for nonlinear trend estimation of non-stationary signals, by which the trend can be self-adaptively decomposed through calculating the midpoint-based local means. In this method, the so-called midpoints are proposed to construct the local mean of a signal instead of two envelopes in the classical empirical mode decomposition (EMD) algorithm, thus resulting in the midpoint-based empirical decomposition. Furthermore, a negentropy-based statistical method is presented to justify decomposition of the trend. Simulation results indicate that the new algorithm improves the performance of signal decomposition and trend estimation in comparison with the classical EMD algorithm. The proposed method also shows the value in self-adaptively estimating the nonlinear respiratory component from non-invasively measured ventilation signals.

Development of statistical regression models for ventilation estimation.

Estimation of ventilation volume from dimensional changes of the rib cage and abdomen is of interest to researchers interested in quantifying internal exposure to environmental pollutants in the atmosphere. In this paper, we present different statistical regression models for estimating ventilation volume during free-living activities. The movements of the rib cage and abdomen were measured by piezoelectric sensor belts. Multiple linear regression as the calibration method was applied. Five regression models with different combinations out of thirteen features were developed and the performance of these models was compared through experimental study of 11 subjects. The effect of training approaches - model trained for each subject and for all subjects, and the effect of time intervals for computing features were also investigated. The results indicate that Model 2, combining respiratory features and breathing frequency, with a longer time intervals will lead to a higher accuracy.

Empirical mode decomposition applied to tissue artifact removal from respiratory signal.

Estimation of respiration commonly employs piezoelectric sensors secured to rib cage and abdominal belts. However, these respiratory signals are often contaminated by tissue artifact. This paper presents a signal decomposition technique for tissue artifact removal in respiratory signals, based on empirical mode decomposition (EMD). After introducing the theoretical foundation, this method is performed on three synthetic signals, and performance of tissue artifact removal using EMD is compared with low-pass filter and independent component analysis (ICA) techniques. A simulation study and experimental results show that EMD can effectively remove tissue artifact in respiratory signals.

Design of ultrasonic transmitters with defined frequency characteristics for wireless pressure sensing in injection molding.

This paper describes a new mechanical wireless data transmission technique using ultrasonic waves as the information carrier for on-line injection mold cavity pressure measurement. Ultrasonic transmitters with specific frequency characteristics were designed, modeled, simulated, and prototyped for pressure data retrieval from an enclosed machine environment, as well as for sensor identification in a sensor matrix configuration. The effects of the front layer and bonding layer of the transmitter on the overall sensor frequency characteristics were investigated, using an equivalent circuit model. The optimal layer thickness was determined for the design of transmitters with specific dominant resonant frequency and narrow bandwidth. Experimental results were in good agreement with the analysis, thus confirming the design approach.