Experiments of posture estimation on vehicles using wearable acceleration sensors

In this paper, we study methods to estimate drivers' posture in vehicles using acceleration data of wearable sensor and conduct a field test. Recently, sensor technologies have been progressed. Solutions of safety management to analyze vital data acquired from wearable sensor and judge work status are proposed. To prevent huge accidents, demands for safety management of bus and taxi are high. However, acceleration of vehicles is added to wearable sensor in vehicles, and there is no guarantee to estimate drivers' posture accurately. Therefore, in this paper, we study methods to estimate driving posture using acceleration data acquired from T-shirt type wearable sensor hitoe, conduct field tests and implement a sample application.Comment: 4 pages, 4 figures, The 3rd IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2017), pp.14-17, Beijing, May 201

Compact personal distributed wearable exposimeter

A compact wearable personal distributed exposimeter (PDE) is proposed, sensing the power density of incident radio frequency (RF) fields on the body of a human. In contrast to current commercial exposimeters, our PDE, being composed of multiple compact personal wearable RF exposimeter sensor modules, minimizes uncertainties caused by the proximity of the body, the specific antenna used, and the exact position of the exposimeter. For unobtrusive deployment inside a jacket, each individual exposimeter sensor module is specifically implemented on the feedplane of a textile patch antenna. The new wearable sensor module's high-resolution logarithmic detector logs RF signal levels. Next, on-board flash memory records minimum, maximum, and average exposure data over a time span of more than two weeks, at a one-second sample period. Sample-level synchronization of each individual exposimeter sensor module enables combining of measurements collected by different nodes. The system is first calibrated in an anechoic chamber, and then compared with a commercially available single-unit exposimeter. Next, the PDE is validated in realistic conditions, by measuring the average RF power density on a human during a walk in an urban environment and comparing the results to spectrum analyzer measurements with a calibrated antenna

A facile approach to fabricate highly sensitive, flexible strain sensor based on elastomeric/graphene platelet composite film

This work developed a facile approach to fabricate highly sensitive and flexible polyurethane/graphene platelets composite film for wearable strain sensor. The composite film was fabricated via layer-by-layer laminating method which is simple and cost-effective; it exhibited outstanding electrical conductivity of 1430 ± 50 S/cm and high sensitivity to strain (the gauge factor is up to 150). In the sensor application test, the flexible strain sensor achieves real-time monitoring accurately for five bio-signals such as pulse movement, finger movement, and cheek movement giving a great potential as wearable-sensing device. In addition, the developed strain sensor shows response to pressure and temperature in a certain region. A multifaceted comparison between reported flexible strain sensors and our strain sensor was made highlighting the advantages of the current work in terms of (1) high sensitivity (gauge factor) and flexibility, (2) facile approach of fabrication, and (3) accurate monitoring for body motions

Wearable sensing application- carbon dioxide monitoring for emergency personnel using wearable sensors

The development of wearable sensing technologies is a great challenge which is being addressed by the Proetex FP6 project (www.proetex.org). Its main aim is the development of wearable sensors to improve the safety and efficiency of emergency personnel. This will be achieved by continuous, real-time monitoring of vital signs, posture, activity, and external hazards surrounding emergency workers. We report here the development of carbon dioxide (CO2) sensing boot by incorporating commercially available CO2 sensor with a wireless platform into the boot assembly. Carefully selected commercially available sensors have been tested. Some of the key characteristics of the selected sensors are high selectivity and sensitivity, robustness and the power demand. This paper discusses some of the results of CO2 sensor tests and sensor integration with wireless data transmission

A low-power opportunistic communication protocol for wearable applications

© 2015 IEEE.Recent trends in wearable applications demand flexible architectures being able to monitor people while they move in free-living environments. Current solutions use either store-download-offline processing or simple communication schemes with real-time streaming of sensor data. This limits the applicability of wearable applications to controlled environments (e.g, clinics, homes, or laboratories), because they need to maintain connectivity with the base station throughout the monitoring process. In this paper, we present the design and implementation of an opportunistic communication framework that simplifies the general use of wearable devices in free-living environments. It relies on a low-power data collection protocol that allows the end user to opportunistically, yet seamlessly manage the transmission of sensor data. We validate the feasibility of the framework by demonstrating its use for swimming, where the normal wireless communication is constantly interfered by the environment

Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks

While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson's Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54\% to 86.88\%.Comment: ICMI2017 (oral session

Wearable activity sensors and early pain after total joint arthroplasty.

A prospective observational cohort of 20 primary total hip arthroplasty (n = 12) and total knee arthroplasty (n = 8) patients (mean age: 63 ± 6 years) was passively monitored with a consumer-level wearable activity sensor before and 6 weeks after surgery. Patients were clustered by minimal change or decreased activity using sensor data. Decreased postoperative activity was associated with greater pain reduction (-5.5 vs -2.0, P = .03). All patients surpassed minimal clinical benefit thresholds of total joint arthroplasty (TJA) (Hip Disability and Osteoarthritis Score Junior 30.5 vs 20.8, P = .23; Knee Injury and Osteoarthritis Outcome Score Junior 23.3 vs 18.2, P = .77) within 6 weeks. Patients who objectively "take it easy" after TJA may experience less pain with no difference in early subjective outcome. Remote, passive analysis of outpatient wearable sensor data may permit real-time detection of early problems after TJA