Towards Wearable and Flexible Sensors and Circuits Integration for Stress Monitoring.

Excessive stress is one of the main causes of mental illness. Long-term exposure of stress could affect one's physiological wellbeing (such as hypertension) and psychological condition (such as depression). Multisensory information such as heart rate variability (HRV) and pH can provide suitable information about mental and physical stress. This paper proposes a novel approach for stress condition monitoring using disposable flexible sensors. By integrating flexible amplifiers with a commercially available flexible polyvinylidene difluoride (PVDF) mechanical deformation sensor and a pH-type chemical sensor, the proposed system can detect arterial pulses from the neck and pH levels from sweat located in the back of the body. The system uses organic thin film transistor (OTFT)-based signal amplification front-end circuits with modifications to accommodate the dynamic signal ranges obtained from the sensors. The OTFTs were manufactured on a low-cost flexible polyethylene naphthalate (PEN) substrate using a coater capable of Roll-to-Roll (R2R) deposition. The proposed system can capture physiological indicators with data interrogated by Near Field Communication (NFC). The device has been successfully tested with healthy subjects, demonstrating its feasibility for real-time stress monitoring.

Transforming Health Care: Body Sensor Networks, Wearables, and the Internet of Things.

This paper talks about body sensor networks, wearables, and the Internet of Things.

Assessing Arthroscopic Skills Using Wireless Elbow-Worn Motion Sensors.

BACKGROUND: Assessment of surgical skill is a critical component of surgical training. Approaches to assessment remain predominantly subjective, although more objective measures such as Global Rating Scales are in use. This study aimed to validate the use of elbow-worn, wireless, miniaturized motion sensors to assess the technical skill of trainees performing arthroscopic procedures in a simulated environment. METHODS: Thirty participants were divided into three groups on the basis of their surgical experience: novices (n = 15), intermediates (n = 10), and experts (n = 5). All participants performed three standardized tasks on an arthroscopic virtual reality simulator while wearing wireless wrist and elbow motion sensors. Video output was recorded and a validated Global Rating Scale was used to assess performance; dexterity metrics were recorded from the simulator. Finally, live motion data were recorded via Bluetooth from the wireless wrist and elbow motion sensors and custom algorithms produced an arthroscopic performance score. RESULTS: Construct validity was demonstrated for all tasks, with Global Rating Scale scores and virtual reality output metrics showing significant differences between novices, intermediates, and experts (p < 0.001). The correlation of the virtual reality path length to the number of hand movements calculated from the wireless sensors was very high (p < 0.001). A comparison of the arthroscopic performance score levels with virtual reality output metrics also showed highly significant differences (p < 0.01). Comparisons of the arthroscopic performance score levels with the Global Rating Scale scores showed strong and highly significant correlations (p < 0.001) for both sensor locations, but those of the elbow-worn sensors were stronger and more significant (p < 0.001) than those of the wrist-worn sensors. CONCLUSIONS: A new wireless assessment of surgical performance system for objective assessment of surgical skills has proven valid for assessing arthroscopic skills. The elbow-worn sensors were shown to achieve an accurate assessment of surgical dexterity and performance. CLINICAL RELEVANCE: The validation of an entirely objective assessment of arthroscopic skill with wireless elbow-worn motion sensors introduces, for the first time, a feasible assessment system for the live operating theater with the added potential to be applied to other surgical and interventional specialties.

Body Sensor Networks: In the Era of Big Data and Beyond.

Body sensor networks (BSN) have emerged as an active field of research to connect and operate sensors within, on or at close proximity to the human body. BSN have unique roles in health applications, particularly to support real-time decision making and therapeutic treatments. Nevertheless, challenges remain in designing BSN nodes with antennas that operate efficiently around, ingested or implanted inside the human body, as well as new methods to process the heterogeneous and growing amount of data on-node and in a distributed system for optimized performance and power consumption. As the battery operating time and sensor size are two important factors in determining the usability of BSN nodes, ultralow power transceivers, energy-aware network protocol, data compression, on-node processing, and energy-harvesting techniques are highly demanded to ultimately achieve a self-powered BSN.

Development of a wireless sensor glove for surgical skills assessment.

Laparoscopic surgery is a challenging task in minimally invasive surgery, which involves complex instrument control, extensive manual dexterity, and hand-eye coordination. This requires a greater attention to training and skills evaluation. In order to provide a more objective skills assessment method, this paper presents a wireless sensor platform for the capture of laparoscopic hand gesture data and a hidden-Markov-model-based analysis framework for optimal sensor selection and placement. Detailed experimental validation is provided to illustrate how the proposed method can be used to assess surgical performance improvement over repeated training.

Invisible shadow for navigation and planning in minimal invasive surgery.

Depth estimation is one of the most fundamental challenges for performing minimally invasive surgical (MIS) procedures. The requirement of accurate 3D instrument navigation using limited visual depth cues makes such tasks even more difficult. With the constant expectation of improving safety for MIS, there is a growing requirement for overcoming such constraints during MIS. We present in this paper a method of improving the surgeon's perception of depth by introducing an "invisible shadow" in the operative field cast by an endoscopic instrument. Although, the shadow is invisible to human perception, it can be digitally detected, enhanced and re-displayed. Initial results from our study suggest that this method improves depth perception especially when the endoscopic instrument is in close proximity to the surface. Experiment results have shown that the method could potentially be used as an instrument navigation aid allowing accurate maneuvering of the instruments whilst minimizing tissue trauma.