Per Pattern-based Calibration Method for EIT Systems

A calibration approach has been developed for use with the EIT systems that are significantly influenced by parasitic impedances associated with switches, multiplexers and channel-to-channel coupling. Calibrated data acquired from saline tank experiments is compared with the data obtained from a forward simulation of the experiment

ActivityAware: an App for Real-Time Daily Activity Level Monitoring on the Amulet Wrist-Worn Device

Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person\u27s daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware, an application on the Amulet wrist-worn device that measures daily activity levels (sedentary, moderate and vigorous) of individuals, continuously and in real-time. The app implements an activity-level detection model, continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day\u27s accumulated time spent at that activity level, logs the data for later analysis, and displays the results on the screen. We developed an activity-level detection model using a Support Vector Machine (SVM). We trained our classifiers using data from a user study, where subjects performed the following physical activities: sit, stand, lay down, walk and run. With 10-fold cross validation and leave-one-subject-out (LOSO) cross validation, we obtained preliminary results that suggest accuracies up to 98%, for n=14 subjects. Testing the ActivityAware app revealed a projected battery life of up to 4 weeks before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring, and eventually for the development of interventions that could improve the health of individuals

A Wearable System that Knows Who Wears It

Body-area networks of pervasive wearable devices are increasingly used for health monitoring, personal assistance, entertainment, and home automation. In an ideal world, a user would simply wear their desired set of devices with no configuration necessary: the devices would discover each other, recognize that they are on the same person, construct a secure communications channel, and recognize the user to which they are attached. In this paper we address a portion of this vision by offering a wearable system that unobtrusively recognizes the person wearing it. Because it can recognize the user, our system can properly label sensor data or personalize interactions. \par Our recognition method uses bioimpedance, a measurement of how tissue responds when exposed to an electrical current. By collecting bioimpedance samples using a small wearable device we designed, our system can determine that (a)the wearer is indeed the expected person and (b) the device is physically on the wearer\u27s body. Our recognition method works with 98% balanced-accuracy under a cross-validation of a day\u27s worth of bioimpedance samples from a cohort of 8 volunteer subjects. We also demonstrate that our system continues to recognize a subset of these subjects even several months later. Finally, we measure the energy requirements of our system as implemented on a Nexus S smart phone and custom-designed module for the Shimmer sensing platform

Development of a ‘Smart’ Resistance Exercise Band to Assess Strength

https://digitalcommons.dartmouth.edu/wetterhahnsymposium-2018/1004/thumbnail.jp

Who Wears Me? Bioimpedance as a Passive Biometric

Mobile and wearable systems for monitoring health are becoming common. If such an mHealth system knows the identity of its wearer, the system can properly label and store data collected by the system. Existing recognition schemes for such mobile applications and pervasive devices are not particularly usable – they require ıt active engagement with the person (e.g., the input of passwords), or they are too easy to fool (e.g., they depend on the presence of a device that is easily stolen or lost). \par We present a wearable sensor to passively recognize people. Our sensor uses the unique electrical properties of a person\u27s body to recognize their identity. More specifically, the sensor uses ıt bioimpedance – a measure of how the body\u27s tissues oppose a tiny applied alternating current – and learns how a person\u27s body uniquely responds to alternating current of different frequencies. In this paper we demonstrate the feasibility of our system by showing its effectiveness at accurately recognizing people in a household 90% of the time

A bioimpedance-based monitor for real-time detection and identification of secondary brain injury

Secondary brain injury impacts patient prognosis and can lead to long-term morbidity and mortality in cases of trauma. Continuous monitoring of secondary injury in acute clinical settings is primarily limited to intracranial pressure (ICP); however, ICP is unable to identify essential underlying etiologies of injury needed to guide treatment (e.g. immediate surgical intervention vs medical management). Here we show that a novel intracranial bioimpedance monitor (BIM) can detect onset of secondary injury, differentiate focal (e.g. hemorrhage) from global (e.g. edema) events, identify underlying etiology and provide localization of an intracranial mass effect. We found in an in vivo porcine model that the BIM detected changes in intracranial volume down to 0.38 mL, differentiated high impedance (e.g. ischemic) from low impedance (e.g. hemorrhagic) injuries (p \u3c 0.001), separated focal from global events (p \u3c 0.001) and provided coarse ‘imaging’ through localization of the mass effect. This work presents for the first time the full design, development, characterization and successful implementation of an intracranial bioimpedance monitor. This BIM technology could be further translated to clinical pathologies including but not limited to traumatic brain injury, intracerebral hemorrhage, stroke, hydrocephalus and post-surgical monitoring

Towards Endoscopic EIT: Ex vivo Assessment of Human Prostates

Following robotic assisted laparoscopic prostatectomy (RALP), surgical margins are assessed for presence of cancerous tissues. An EIS/EIT based approach to identify benign and malignant tissues was evaluated. Excised prostates were probed using a micro-endoscopic EIT probe, and impedance measurements corresponding to benign and tumorous regions are compared

MR-EPT Reconstruction Using an Inverse Formulation

The electrical conductivity of soft tissues can be reconstructed from imaging with MR Electrical Properties Tomography (MR-EPT). The reconstruction method used here is based on an inverse problem formulation, with two advantages over a direct inversion approach: a) no spatial differentiation is needed and b) the regularization term determines the resolution of the reconstructed data. The process is exemplified using phantom (gelatine and saline) data

Design of a Microendoscopic EIT Probe: A Simulation Study

We describe a simulation study evaluating different electrode configuration for a microendoscopic EIT probe intended to intraoperatively assess surgical margins during radical prostatectomy. In our simulation study, we analyze the performances of three probe designs with varying number of electrodes (8, 9, and 17) and configurations (dependent on number of electrodes)