... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks最新文献

Timely decision making is critical to the effectiveness of mobile health (mHealth) interventions. At predefined timepoints called "decision points," intelligent mHealth systems such as just-in-time adaptive interventions (JITAIs) estimate an individual's biobehavioral context from sensor or survey data and determine whether and how to intervene. For interventions targeting habitual behavior (e.g., oral hygiene), effectiveness often hinges on delivering support shortly before the target behavior is likely to occur. Current practice schedules decision points at a fixed interval (e.g., one hour) before user-provided behavior times, and the fixed interval is kept the same for all individuals. However, this one-size-fits-all approach performs poorly for individuals with irregular routines, often scheduling decision points after the target behavior has already occurred, rendering interventions ineffective. In this paper, we propose SigmaScheduling, a method to dynamically schedule decision points based on uncertainty in predicted behavior times. When behavior timing is more predictable, SigmaScheduling schedules decision points closer to the predicted behavior time; when timing is less certain, SigmaScheduling schedules decision points earlier, increasing the likelihood of timely intervention. We evaluated SigmaScheduling using real-world data from 68 participants in a 10-week trial of Oralytics, a JITAI designed to improve daily toothbrushing. SigmaScheduling increased the likelihood that decision points preceded brushing events in at least 70% of cases, preserving opportunities to intervene and impact behavior. Our results indicate that SigmaScheduling can advance precision mHealth, particularly for JITAIs targeting time-sensitive, habitual behaviors such as oral hygiene or dietary habits.

Electrodermal activity (EDA) provides a direct indicator of sympathetic nervous system arousal through changes in skin conductance. However, wearable EDA sensing poses challenges such as inconsistent skin contact, electrode impedance variability, motion artifacts, and power constraints. To address these issues, this study presents mobile EDA (mEDA), a compact device driven by a stabilized direct-current source. A validation study was conducted on ten healthy adult participants in a time-synchronized protocol to collect data from BIOPAC and mEDA concurrently. mEDA recordings employed gel electrodes for P1-P5 and dry (textile) electrodes for P6-P10, while the BIOPAC MP160 system used gel electrodes for all participants. Participants underwent a 30-minute protocol of resting, deep breathing, and three cognitive tasks. The preprocessing pipeline consisted of low-pass filter and artifact (sharp peaks and flat line) removal. Cleaned signals were converted into frequency domain components for decomposition into low and high frequency components, skin conductance level (SCL), and skin conductance response (SCR) respectively. SCL and SCR were converted back to the time domain to analyze performance metrics between both devices. Pearson correlation, coherence, and Dynamic Time Warping (DTW) were computed on SCL, while zero-crossing peaks were counted for SCR analysis. With gel electrodes, the average Pearson correlation was 0.92 and the SCR peak count difference was 38. For textile electrodes, the correlation was 0.88 with a peak count difference of 119. Both configurations achieved coherence above 0.95 and DTW below 0.5 for most participants. These results demonstrate mEDA's reliable performance in capturing both tonic and phasic EDA across electrode configurations.

Skin cancer, particularly melanoma, is a major health concern due to rising incidence rates, largely driven by ultraviolet (UV) radiation overexposure, making it essential to monitor and manage sun exposure effectively. While existing wearable UV sensors track exposure, they often rely on external power sources, limiting their battery lifetime. This study presents a self-sustaining wearable UV sensor that integrates solar energy harvesting, enabling continuous monitoring without need for frequent recharging. The device uses low-power components to measure UVA and UVB radiation with high accuracy. It is powered by a solar panel made from Ethylene Tetrafluoroethylene (ETFE), which provides continuous energy to recharge a LiPo battery. It transmits data via BLE for real-time feedback and can be used for personalized sun protection recommendations. A usability study with 10 participants demonstrated the sensor's effectiveness in raising UV awareness and encouraging sun protection habits.

Wearable camera and thermal sensing systems are increasingly used for real-time eating detection and timely notifications to remind users to log their meals. However, confounding gestures such as irrelevant hand movements can cause false device confirmations of eating in real-time. Delaying the device confirmation of an eating episode, until the system is certain, can improve accuracy of eating detection, but prevents the capture of shorter bouts of eating. Balancing the trade-off between errors and detection delay is key to developing effective methods that provide immediate user feedback. This paper presents a real-time, hand-object-based method for automated detection of eating and drinking gestures and identifies the minimum number of gestures needed to reliably detect an eating episode. Unlike prior work, our method considers both hand motion and the object-in-hand and uses a low-power thermal sensor to reduce false positives. We evaluated our method on 36 participants, 28 of whom wore a wearable camera for up to 14 days in free-living environments. The results show that eating episodes can be accurately detected using 10 gestures or within the first 1.5 minutes of the eating episode, achieving an F1-score of 89.0%. Our findings provide evaluation guidelines for designing real-time intervention systems to address problematic eating behaviors.

Adherence to medications is a complex task that requires complex biobehavioral support. To better provide tools to assist with medication adherence, digital pills provide an option to directly measure medication taking behaviors. These systems comprise a gelatin capsule with radiofrequency emitter, a wearable Reader that collects the radio signal and a smartphone app that collects ingestion data displays it for patients and clinicians. These systems are feasible in measuring adherence in the real-world, even in stigmatized diseases like HIV treatment adherence. While the current iteration of the digital pill system utilizes a wearable Reader worn like a necklace, preliminary feedback demonstrated that a miniaturized system that was worn on the wrist could be more functional in the real-world. This paper therefore describes the development and preliminary field testing of a wrist-borne wearable Reader to facilitate acquisition of oral HIV pre-exposure prophylaxis (PrEP) adherence data among individual prescribed PrEP.

Wearable devices with embedded sensors can provide personalized healthcare and wellness benefits in digital phenotyping and adaptive interventions. However, the collection, storage, and transmission of biometric data (including processed features rather than raw signals) from these devices pose significant privacy concerns. This quantitative, data-driven study examines the privacy risks associated with wearable-based digital phenotyping practices, with a focus on user reidentification (ReID), which is the process of identifying participants' IDs from deidentified digital phenotyping datasets. We propose a machine-learning-based computational pipeline to evaluate and quantify model outcomes under various configurations, such as modality inclusion, window length, and feature type and format, to investigate the factors influencing ReID risks and their predictive trade-offs. This pipeline leverages features extracted from three wearable sensors, resulting in up to 68.43% accuracy in ReID risk for a sample size of N=45 socially anxious participants based on only descriptive features of 10-second observations. Additionally, we explore the trade-offs between privacy risks and predictive benefits by adjusting various settings (e.g., the ways to process extracted features). Our findings highlight the importance of privacy in digital phenotyping and suggest potential future directions.

Bio-impedance spectroscopy (BIS) is a sophisticated testing technique used to analyze impedance changes at different frequencies. In this study, we investigated the estimation of the Cole Model for BIS measurements without the need for high-frequency resistance and reactance measurements, where they are inaccurate due to leakage capacitences. We employed a Texas Instruments evaluation kit (AFE4300) and compared the Cole plots of two different circuit models of tissue between the proposed configuration and a commercial impedance analyzer used as a reference. To enhance the performance of the AFE4300, we incorporated an external direct digital synthesis (DDS) to generate higher frequencies. The results demonstrated the reliability of the proposed theoretical estimation technique in accurately estimating the resistances and capacitance of the Cole Model.