Wearable Measurement Device Research Articles

Headset bio-sniffer with wireless CMOS camera for percutaneous ethanol vapor from the ear canal

Volatile organic compounds contained in human volatiles are related to metabolism and disease. Measurement of these components is expected to enable non-invasive and simple metabolic evaluations and disease diagnoses. In particular, skin gas can be collected continuously without constraints, a useful property for the next generation of wearable measurement devices. In this study, we develop a wearable biochemical gas sensor (biosniffer) for ethanol by constructing a headset-type unrestrained ear canal gas measurement system integrated with a complementary metal-oxide semiconductor (CMOS) camera. This system measures ethanol gas released by the skin by detecting the increase in fluorescence of NADH, which is produced during the oxidation of ethanol by alcohol dehydrogenase (ADH). In this system, excitation light from a UV-LED irradiates an ADH enzyme-immobilized membrane through a band-pass filter, and the fluorescence of NADH is detected by a small CMOS camera with Wi-Fi functionality. This system is integrated into an earmuff, forming a wearable headset. Fluorescence images shows an increased output correlated with the ethanol gas concentration. When integral analysis is used to assess the increase in fluorescence, it is possible to measure ethanol gas at 11 ppb–444 ppm, a range that includes the ethanol concentration in the skin after alcohol administration. Furthermore, when this device is attached to a human participant who is consuming alcohol, the increase and decrease output based on drinking and metabolism is confirmed, indicating the possibility of directly measuring ethanol gas from the ear canal with minimal effect of perspiration.

A Multi-parameter Joint Warning Mechanism for Physical Condition Monitoring System in Physical Education

Background: In recent years, sudden deaths of primary and secondary school students caused by sports activities have drawn great attention in education and medical circles. It is necessary for schools to monitor the physical condition of the students in order to reasonably set the duration of their physical activity. At present, the physical condition monitoring instruments used in various hospitals are expensive, bulky, and difficult to operate, and the detection process is complicated. Therefore, existing approaches cannot meet the needs of physical education teachers on campus for detecting the physical condition of students. Methods: This study designs a portable human-physiological-state monitoring and analysis system. Real-time communication between a wearable measurement device and a monitoring device can be ensured by real-time detection of the environment and power control of the transmitted signal. Results: From a theoretical point of view, the larger the number of segments M, the more significantly the reduction of false alarm probability. The simulation results also show this fact. Compared with the conventional early warning mechanism, the probability of a false alarm for the proposed system is lower, and the greater the number of segments, the faster its reaction speed. Conclusion: The portable monitoring system of student physical condition for use in physical education of primary and middle school students proposed in this paper ensures real-time monitoring of the members within the system in an open environment, and further proposes an early warning mechanism for combining multiple vital sign parameters. In addition, the proposed system functions faster; the average early warning time required is only one-quarter of that of the conventional system.

Quantitative evaluation of hemiplegic gait characteristic using wearable measurement device

Quantitative evaluation of hemiplegic gait characteristic using wearable measurement device

Improving Kinematic Accuracy of Soft Wearable Data Gloves by Optimizing Sensor Locations.

Bending sensors enable compact, wearable designs when used for measuring hand configurations in data gloves. While existing data gloves can accurately measure angular displacement of the finger and distal thumb joints, accurate measurement of thumb carpometacarpal (CMC) joint movements remains challenging due to crosstalk between the multi-sensor outputs required to measure the degrees of freedom (DOF). To properly measure CMC-joint configurations, sensor locations that minimize sensor crosstalk must be identified. This paper presents a novel approach to identifying optimal sensor locations. Three-dimensional hand surface data from ten subjects was collected in multiple thumb postures with varied CMC-joint flexion and abduction angles. For each posture, scanned CMC-joint contours were used to estimate CMC-joint flexion and abduction angles by varying the positions and orientations of two bending sensors. Optimal sensor locations were estimated by the least squares method, which minimized the difference between the true CMC-joint angles and the joint angle estimates. Finally, the resultant optimal sensor locations were experimentally validated. Placing sensors at the optimal locations, CMC-joint angle measurement accuracies improved (flexion, 2.8° ± 1.9°; abduction, 1.9° ± 1.2°). The proposed method for improving the accuracy of the sensing system can be extended to other types of soft wearable measurement devices.

独立成分分析を用いた胎児の心電信号測定に適する小面積・低消費電力多チャネル増幅回路

A wearable fetal heart rate measuring device is desired for the early detection of the abnormal development of a fetus. A low noise amplifier which occupies small chip area is indispensable for implementation of the wearable measurement device because a multi-point measurement is necessary for accurate measurement using an independent component analysis. In this paper we propose a novel multi-channel amplifier for biomedical applications. A reference input terminal of the proposed amplifier is shared by plural sub-amplifiers to reduce the power consumption. Hspice simulations show that the power consumption of the proposed circuit with 4 inputs is 39% lower than that of a conventional amplifier. It is shown that an independent component analysis based on Fast ICA can extract a fetal electrocardiogram from the output voltages of the proposed amplifier.

Bioimpedance-Based Wearable Measurement Instrumentation for Studying the Autonomic Nerve System Response to Stressful Working Conditions

The assessment of mental stress on workers under hard and stressful conditions is critical to identify which workers are not ready to undertake a mission that might put in risk their own life and the life of others. The ATREC project aims to enable Real Time Assessment of Mental Stress of the Spanish Armed Forces during military activities. Integrating sensors with garments and using wearable measurement devices, the following physiological measurements were recorded: heart and respiration rate, skin galvanic response as well as peripheral temperature. The measuring garments are the following: a sensorized glove, an upper-arm strap and a repositionable textrode chest strap system with 6 textrodes. The implemented textile-enabled instrumentation contains: one skin galvanometer, two temperature sensors, for skin and environmental, and an Impedance Cardiographer/Pneumographer containing a 1 channel ECG amplifier to record cardiogenic biopotentials. The implemented wearable systems operated accordingly to the specifications and are ready to be used for the mental stress experiments that will be executed in the coming phases of the project in healthy volunteers.

Steps towards a miniaturized, robust and autonomous measurement device for the long-term monitoring of patient activity: ActiBelt

We describe the first steps in the development of a wearable measurement device for measuring a subject's three-dimensional acceleration. The ultimate aim is a standard measurement instrument integrated in a belt buckle that allows objective evaluation of treatment and rehabilitation measures in patients, in particular for disabling chronic diseases such as multiple sclerosis. In a first step we combined standard hardware elements to record test data from healthy volunteers. We then developed algorithms to automatically distinguish between different stages of activity, such as jogging, walking, lying, standing and sitting, and to detect and count steps. Distinction between standing and sitting is the most difficult to accomplish. As a first validation, we calculated the distance traveled from data of 17 experiments and a total of 4.5 h, for which one proband was walking and running for a known distance, and compared the results with two commercially available pedometers. We could show that the relative error for the ActiBelt is only half of that for the two pedometers. Apart from developing much smaller, robust and integrated hardware, we describe ideas on how to develop algorithms that allow extraction of a "baseline step pattern" in analogy to baseline ECG to define and detect clinically relevant deviations.

Regression Techniques for the Prediction of Lower Limb Kinematics

This work presents a novel and extensive investigation of mathematical regression techniques, for the prediction of laboratory-type kinematic measurements during human gait, from wearable measurement devices, such as gyroscopes and accelerometers. Specifically, we examine the hypothesis of predicting the segmental angles of the legs (left and right foot, shank and thighs), from rotational foot velocities and translational foot accelerations. This first investigation is based on kinematic data emulated from motion-capture laboratory equipment. We employ eight established regression algorithms with different properties, ranging from linear methods and neural networks with polynomial support and expanded nonlinearities, to radial basis functions, nearest neighbors and kernel density methods. Data from five gait cycles of eight subjects are used to perform both inter-subject and intra-subject assessments of the prediction capabilities of each algorithm, using cross-validation resampling methods. Regarding the algorithmic suitability to gait prediction, results strongly indicate that nonparametric methods, such as nearest neighbors and kernel density based, are particularly advantageous. Numerical results show high average prediction accuracy (rho = 0.98/0.99, RMS = 5.63 degrees/2.30 degrees, MAD = 4.43 degrees/1.52 degrees for inter/intra-subject testing). The presented work provides a promising and motivating investigation on the feasibility of cost-effective wearable devices used to acquire large volumes of data that are currently collected only from complex laboratory environments.