Keywords: Health Assessment, Symptom monitoring & assessment
Abstract: With the flourishing development of body sensor networks, a variety of head-worn sensor-based devices have emerged in many domains, to facilitate applications involving head movements. This paper explores the potential of using head-mounted sensors coupled with computational algorithms, to assess visual field defects through analyzing head motion in reading activities. Visual field defects, such as homonymous hemianopia, is a common disorder that occurs after stroke, injury, or vascular brain damage. A customized reading experiment is conducted on 17 participants, while Google Glass is used for head motion monitoring and visual field defect simulation. The results show a 6%-10% drop in reading performance with the simulated condition. Several machine learnig algorithms demonstrate the distinguishability of head motion in reading activities for visual field defects, with an average accuracy of 91%. Furthermore, experiment results suggest that the difference in head motion between normal and impaired visual field is less significant under extreme reading conditions.

Keywords: Health Assessment, Computational tools, Human models
Abstract: This study reports a new technique for extracting muscle synergies using continuous wavelet transform. The method allows to quantify coincident activation of muscle groups caused by the physiological processes of fixed duration, thus enabling the extraction of wavelet modules of arbitrary groups of muscles. Hierarchical clustering and identification of the repeating wavelet modules across subjects and across movements, was used to identify consistent muscle synergies. Results indicate that the most frequently repeated wavelet modules comprised combinations of two muscles that are not traditional agonists and span different joints. We have also found that these wavelet modules were flexibly combined across different movement directions in a pattern resembling directional tuning. This method is extendable to multiple frequency domains and signal modalities.

Keywords: Multi sensor data fusion, Minimally Invasive Sensors
Abstract: As the Internet of Things (IoT) continues to expand into our daily lives, consumers are finding a growing catalogue of smart devices to boost the intelligence of their homes. Currently, the user must manage a proprietary user interface (UI) for each device, and each application comes with its own UI, creating a cumbersome app environment. Clearly, a single UI that can control all of these devices would be preferable. This interface should be accessible using forms of communication that feel natural, for example, speech, body language, and facial expressions, to name a few. In this paper, we propose a framework for multimodal UI using a flexible, slotted command ontology and decision-level Bayesian fusion. Our case study explores command recognition for device control with a wearable system accessed via speech and gestures, using a wrist-mounted inertial measurement unit (IMU) for hand gesture recognition. We achieve an accuracy of 94.82% on a set of 17 commands.

Keywords: Gait analysis, Minimally Invasive Sensors, Everyday health status
Abstract: This paper proposes a novel artificial neural network based method for real-time gait analysis with minimal number of Inertial Measurement Units (IMUs). Accurate lower limb attitude estimation has great potential for clinical gait diagnosis for orthopaedic patients and patients with neurological diseases. However, the use of multiple wearable sensors hinder the ubiquitous use of inertial sensors for detailed gait analysis. This paper proposes the use of two IMUs mounted on the shoes to estimate the IMU signals at the shin, thigh and waist for accurate attitude estimation of the lower limbs. By using the artificial neural network framework, the gait parameters, such as angle, velocity and displacements of the IMUs can be estimated. The experimental results have shown that the proposed method can accurately estimate the IMUs signals on the lower limbs based only on the IMU signals on the shoes, which demonstrates its potential for lower limb motion tracking and real-time gait analysis.

Keywords: Feature discovery, Computational tools, Low power sensor design
Abstract: Hand motion tracking traditionally requires highly complex and expensive systems in terms of energy and computational demands. A low-power, low-cost system could lead to a revolution in this field as it would not require complex hardware while representing an infrastructure-less ultra-miniature (~ 100μm - [1]) solution. The present paper exploits the Multiple Point Tracking algorithm developed at the Tyndall National Institute as the basic algorithm to perform a series of gesture recognition tasks. The hardware relies upon the combination of a stereoscopic vision of two novel Lensless Smart Sensors (LSS) combined with IR filters and five hand-held LEDs to track. Tracking common gestures generates a six-gestures dataset, which is then employed to train three Machine Learning models: k-Nearest Neighbors, Support Vector Machine and Random Forest. An offline analysis highlights how different LEDs’ positions on the hand affect the classification accuracy. The comparison shows how the Random Forest outperforms the other two models with a classification accuracy of 90-91 %.

Keywords: Symptom monitoring & assessment, Human models, Mood
Abstract: Wearable devices fitted with various sensors are increasingly being used for the automatic and continuous tracking and monitoring of patients. Only first steps have been taken in the field of psychiatric care, where long term tracking of patient behavior holds the promise to help practitioners to better understand both individual patients, and the disorders in general. In this paper we use topic models for unsupervised analysis of movement activity of schizophrenia patients in a closed ward setting. Results demonstrate that features computed on the basis of this analysis differentially characterize interesting sub-populations of schizophrenia patients. Positive-signs schizophrenia sub-population was found to have high motor richness and low typicality, while negative-signs patients had low motor richness and lower typicality. In addition we design a classifier which correctly classified up to 80% of the clinical sub-population (f-score=0.774) based on motor features.

Keywords: Minimally Invasive Sensors, Everyday health status, Computational tools
Abstract: We describe an evaluation of photoplethysmography (PPG) signals with two wavelengths channels (infrared and red) using a wrist-worn sensor for the estimation of heart rate variability (HRV) and oxygen saturation (SpO2). Five healthy subjects were equipped with a commercial wrist-worn pulse oximeter (Wavelet Health, USA) on the right hand, and both a commercial smart watch for fitness use (Huawei Watch, Series 2) and a clinically-validated transmission-mode pulse oximeter (Creative Medical PC-68B) on their left hand as a reference. Synchronised PPG signals from the Wavelet Health, the Huawei watch, and the PC-68B were recorded for approximately 10 minutes. Subjects were asked to leave the left hand in a resting state, while moving the right hand with two types of movement (periodic and random). A method is proposed to incorporate coupling information between the two wavelengths of PPGs based on the bivariate empirical mode decomposition algorithm. Our method is shown to improve the quality of red PPG allowing improved estimation of SpO2. A comparison of average heart rate (HR), HRV, and SpO2 from all devices is provided.

Keywords: Multi sensor data fusion, Health Assessment, Chronic disease management
Abstract: Ankle-Brachial Pressure Index (ABI), the ratio of ankle blood pressure to arm pressure, has been used to assess the severity of peripheral arterial disease (PAD) and to predict major cardiovascular events. This study proposes three indices which can be readily obtained from electrocardiogram and photoplethysmogram, and studies their correlation with ABI in supine and sitting positions. The proposed indices were derived from pulse arrival time (PAT). Specifically, the ratio of PAT measured from the fingertips to PAT measured from the toes of both sides (PATratio), and the relative bilateral differences in PAT measured from the toes of the legs, normalised by average PAT from the two legs (PATdiff1) and by the minimal PAT from one side (PATdiff2) were calculated. The results showed that PATratio is better correlated with ABI compared to PATdiff1 and PATdiff2. The coefficient of determination (R2) between PATratio and ABI is 0.57 when measured in sitting posture, indicating its potential role as an alternative index of ABI for PAD diagnosis. Compared with ABI, the advantages of PATratio are 1) it can be measured at ease with wearable sensors that are available at a relatively low cost; 2) the PAT-derived indices can be measured in a sitting posture which is more convenient to obtain; and 3) it can potentially provide longer term, continuous monitoring of the vascular function.

Keywords: Actionable User Feedback, Minimally Invasive Sensors
Abstract: Stress or anxiety disorders are a growing problem in industrialized countries. Those can be effectively countered by several relaxation techniques which are more effective using biofeedback. Modern virtual reality hardware provides a high level of immersion to its users. This directly affects the feeling of presence. An increased feeling of presence may allow biofeedback mechanisms to work more effectively. We build on this idea and explore how different visualizations of a user's cardiac activity in a virtual environment can be used in biofeedback scenarios - and how effective they are. Using a state-of-the-art virtual reality headset, 14 participants were subjected to four different visualizations of their own heart rate (one control visualization and three experimental visualizations). In different experiments, we examined whether they were able to estimate their heart rate based on the visualization and whether we could influence it subconsciously. Furthermore, we used the AttrakDiff questionnaire to assess the usability and attractiveness of each of the four visualizations. For the three non-reference ones, we observed significant positively correlating changes in the heart rate between the real-time true representation of the heart rate and a simulated increased heart rate visualization with a mean magnitude of 1.96+-0.39 beats per minute. The results from the source and number estimation experiments and the questionnaire led to the conclusion that the most appealing and best working visualization for biofeedback is a synchronized modulation/modification of the virtual environment itself.

Keywords: Everyday health status, Low power sensor design, Electrotextiles
Abstract: Users of wearable devices often hope to collect continuous personal vital signs such as heartrates, blood pressures, respiration rates and breath efforts with comfort and free movement. Current approaches are however hindered by their accuracy, comfort, convenience, power consumptions and sensing capabilities. The requirement of direct skin contacts in electrocardiogram (ECG) and acoustic methods limits their sensation of comfort, wearing convenience, body motion and hence long-term use. Photo-plethysmography (PPG) in wrist watches and finger clamps do not require immediate skin touch but its signal can be severely degraded by relative movement. In this paper, we introduce a new method modulating the external and internal motion of the wearer body directly onto multiplexed radio frequency (RF) signals by near-field coherent sensing (NCS). To minimize the deployment and maintenance cost of NCS vital-sign monitoring, passive (battery-free) RF identification (RFID) tags can be integrated into garments at the chest and wrist areas as laundry-ready wearable devices. NCS utilizes both amplitude and phase of the RF signals to sense and isolate the vital signs, which significantly increases the signal quality in comparison to methods based solely on signal strength. The high frequency components of the NCS signal is employed to mitigate the body movement interference to obtain more accurate heartrates.

Keywords: Health Assessment, Quality of data, Minimally Invasive Sensors
Abstract: Wearables with embedded electrodes and sensors are capable of continuously performing Electrocardiography (ECG), recording electrical activity of the heart, while estimating heart-rate of the wearer. Recent advances in wearable technology have generated comfortable flexible sensors that conform to the contours of the body and can measure heart-rate in the field by capturing QRS complexes of ECG signals. Due to various activities of daily living (ADL), skin deformation by means of lateral, rotational or skin stretching can cause changes in the current pathways of the sensor creating noisy data. The challenge is to disentangle the noise from the usable data in order to accurately detect QRS complexes of ECG signals. In this paper we design a framework to capture noise from a miniaturized flexible sensor, the Biostamp (with 4 leads), worn on the chest of 16 participants performing a set of structured ADL in a home setting, and a baseball player (pitcher). We present a machine-learning framework using a consensus fusion classifier comprising a Support Vector Machine and Neural Network learned model to remove noise while preserving neighboring R-peaks. We evaluate the model using Leave One Subject Out (LOSO) and yield an average of 83% F-measure on the 17 participants (including the pitcher). The low false negative rate provides accurate heart-rate detection on a fine-grained (every 5 seconds) level, in the presence of intermittent stretching of the skin. Our results increase the reliability of detecting heart-rate in real-world and player settings, increasing the utility of flexible ECG-based sensors in the field.

Keywords: Heart disease, Symptom monitoring & assessment, Medication adherence & management
Abstract: A visualisation method for automatically clustering heart beats in single channel electrocardiography (ECG) signals was developed and applied to a dataset of post-intensive care patients who received long-term continuous monitoring. We first segmented the ECG signal into individual beats using an R-peak detection algorithm. A matrix was constructed by storing the segmented ECG beats in row-wise format. Singular value decomposition (SVD) was applied to remove sparse invalid detected R peaks, thus smoothing the matrix. Treating the matrix of ECG beat values as an image, an edge detection algorithm was applied, resulting in a binary matrix containing traces of heart beats with the contiguous and discontiguous components extracted. We considered each component to be a cluster of heart beats. This method was robust to signal noise by exploiting detected R peaks and ECG raw cycles represented in a matrix format for estimation of heart beats. The algorithm also eliminated the effect of underestimated R peaks in the estimation of heart beats, and minimised the effects of overestimated R peaks using the SVD algorithm. This method allows clusters of beats to be visualized, which may assist clinicians in estimating the components of long-term ECG signals.

Keywords: Minimally Invasive Sensors, Health Assessment
Abstract: Patch sensors are slowly becoming ubiquitous in the market for wearable devices. They can collect and send data unobtrusively, which is beneficial to several applications in healthcare, sports, and fitness industries. Patch sensor by inkjet printing (IJP) with functional materials on flexible substrates allows design flexibility and portability at a very low cost. In this paper, preliminary results on characterization of IJP resistors printed with a variety of inks on substrates such as paper and polyimide have been reported, followed by a discussion on printed breath-rate and ECG sensors. The results promise a plethora of possibilities in printed circuits.

Keywords: Electrotextiles, Extreme Performance and Limits of Performance, Heart disease
Abstract: A novel dry disposable electrode using carbonized foam as conductive material is presented. The conductive material is flexible and the manufacturing of it is inexpensive. In this paper, the preparation of the conductive material and the electrical properties of the electrode are investigated. A test protocol is designed to compare the in-vitro impedance, skin-electrode interface and the signal quality of the proposed electrode with that of the wet Ag/AgCl electrode. Experimental results reveal that the carbonized foam has good flexibility and conductivity. The proposed electrode can acquire ECG signal of promising signal quality when compared with Ag/AgCl electrode in the case of static and motion. Furthermore, raw data with less power line interference was observed by proposed electrodes without noise suppression circuits or algorithms. All these make the novel electrode a promising candidate for wearable bio-potential recording.

Keywords: Actionable User Feedback, Symptom monitoring & assessment
Abstract: In this paper, we present a wearable mouse controller based on the pressure sensors, which can recognize the hand gestures in real-time and translate them into the movements of mouse. Only four force sensitive resistors (FSRs) are placed around the wrist to capture the wrist pressure distribution of different hand gestures. A novel real-time recognition framework is proposed which can accurately and quickly identify the starting and ending position of the gesture. In the proposed framework, a pressure-parameter adaptive updating strategy is designed for improving the robust of the system. For different hand positions, different users and re-wear, the system still has a good performance. To ensure the real-time of the system, we choose some simple features and classifiers to recognize hand gestures for decreasing the time complexity. In addition, the WristMouse prototype has a small volume, a low energy consumption and is easy to integrate with the normal wearable devices like smart watch and smart wristband. Our study shows that the proposed system has a good performance in real-time hand gesture recognition with a high F-score of 92.55%, and the average time delay per second is less than 50ms for processing data.

Keywords: Cognitive impairment, Health Assessment
Abstract: Hand gestures are expressive motions that convey meaningful information. The ability for machines to extract and process the underlying meanings of these gestures is critical to many human-interactive applications. Various methods have been proposed, but the development of a more accurate, and simpler system could enable the machine and its user to exchange useful information more effectively. In this paper, a barometric-pressure-sensor-based wristband is presented as an initial proof of such concept. The wristband is composed of an array of 10 barometric pressure sensors spaced evenly around the wrist to estimate pressure profiles as tendons and muscles change with various hand gestures. Subject testing was performed to quantify classification accuracy for three groups of hand gestures: group 1) six wrist gestures, group 2) five single finger flexions, and group 3) ten Chinese number gestures. Leave-one-out cross-validation was used to compute classification accuracy. Results demonstrated classification accuracies of 98% for the wrist gestures, 95% for the single finger flexions, and 90% for Chinese number gestures. The presented pressure sensing wristband could potentially be used for a variety of applications including gesture-controlled devices, health-monitoring devices, and assistive devices for deaf-mute individuals.

Keywords: Chronic pain management, Health Assessment, Chronic disease management
Abstract: Motor learning is the ability to acquire a new motor skill, which plays an important role in rehabilitation as patients learn exercise programs or modify movements to regain pain free function. In this paper, we design an easy-to-use multimodal sensor system to assess motor learning. We developed a motor learning assessment device with a touch screen and Leap Motion to record the subject hand movement during a Serial Reaction Time Task(SRTT). The SRTT consists of upper limb reaching to targets in multi-dimensions. The device records metrics of time and movement efficiency and examines motor learning based on data analysis. This device can provide clinicians with data that can inform their approach to training. We recruited a total of 11 participants, with and without chronic pain to evaluate the device using a classifier model to assess participants' performance. The model shows our system works well to identify motor learning differences in individuals with and without chronic pain.

Keywords: Electrotextiles, Minimally Invasive Sensors, Symptom monitoring & assessment
Abstract: Electrooculography (EOG) is a well-known approach to analyze eye movement features. Applications of EOG can be found in various areas including medical diagnosis, neurosciences, control systems, sensors and interfaces for human-computer interaction (HCI). However, standard gel-based electrodes limit wearability and portability which hinder development of long-term EOG monitoring applications. To overcome these limitations, we have employed graphene-coated fabric electrodes as suitable alternatives for the currently used silver/silver chloride (Ag/AgCl) “wet” electrodes. Proof of the concept is provided by side by side comparison of conventional electrodes and fabric electrodes in automatic blink detection with sequential multi-step thresholding algorithm. Additionally, the EOG biopotentials are converted into real-time digital signals which could be used as clock signals to facilitate development of HCI applications.

Keywords: Low power sensor design, Power optimization, Minimally Invasive Sensors
Abstract: This study presents an ultra-low resource platform for physiological sensing that uses on-chip wavelet compression to enable long-term recording of electrodermal activity (EDA) within a 64kB microcontroller. The design is implemented on a wearable platform and provides improvements in size and power compared to existing wearable technologies and was used in a lab setting to monitor EDA of 27 participants throughout a stress induction protocol. We demonstrate the device's sensitivity to stress induction by providing descriptive statistics of 8 common EDA signal features for each stressor of the experiment. To the best of our knowledge, this is the first time a generic, 16-bit microcontroller (MCU) has been used to record real-time physiological signals on a wearable platform without the use of external memory chips or wireless transmission for extended periods of time. The compression techniques described can lead to reductions in size, power, and cost of wearable biosensors with little or no modifications to existing sensor hardware and could be valuable for applications interested in monitoring long-term physiological trends at lower data rates and memory requirements.

Keywords: Power optimization
Abstract: This paper presents a motion-adaptive approach to duty-cycling the orientation estimation of a wearable inertial measurement unit (IMU). Specifically, a proportional forward-controller was employed to dynamically tune the sampling and orientation update rate of a Madgwick filter. An energy model was defined to estimate the power consumed by individual inertial sensors and processing elements. We demonstrate the efficacy of our controller by analysing multi-day free-living motion recordings of wrist-worn IMUs. In a comparison of the orientation estimation between the full duty-cycle and the adaptive one, average error was approx. 10~degrees while saving more than 30% of sensor node energy. To assess the orientation information retained by our approach, we analysed the binary pattern classification performance for recognising food and fluid intake gestures. Recognition performance of the motion-adaptive duty-cycling remained above 90% up to an energy saving of approx. 32.5%, confirming the profound potential of the method.

Keywords: Energy harvesting, Hydration Status, Environmental Exposures Monitoring
Abstract: Skin cancer is a medical condition that is becoming more common in many countries as a result of excessive exposure of individuals to sunlight. The ultraviolet range of the electromagnetic radiation is responsible for 90% of the cases involving the development of melanomas. Additional factors like the skin tone and texture can increase the risk of radiation exposure when the water content retained by the skin starts to drop dramatically. In this paper we present a small, battery-less wearable device that combines the computation of sunlight exposure with the measurement of the impedance of the skin and temperature, at any time of the day and independently of the location of the person wearing the sensor. Results have shown a good performance in tracking the ultraviolet index and the variation of impedance for different levels of skin hydration

Keywords: Cognitive impairment, Everyday health status, Minimally Invasive Sensors
Abstract: In this paper, we present a lightweight, on-body, wireless system designed for monitoring real-world, ambulatory voice characteristics. The system has the potential to provide important assessments of voice and speech disorders and the impact of environmental sound levels as individuals go about their daily life. The system’s transmitter is positioned on the neck and synchronously streams dual-channel sensor data from an on-board MEMS microphone and a high-bandwidth accelerometer, which acts as a noise-robust and confidential contact microphone. These data are recorded to a receiver that can store the data locally and stream a real-time feed to a computer. We also report on the design considerations of this novel system and discuss progress leading up to the latest iteration, especially of the transmitter components on a flexible circuit. Pilot data are shown from an in-field, ambulatory recording during an individual’s daily activities that included settings in quiet and with naturalistic ambient noise.

Keywords: Power optimization, Energy harvesting, Health Assessment
Abstract: While body sensor networks (BSNs) have proven to be a feasible solution for long-term vigilant health monitoring, limited battery life remains one of the main factors that has impeded their widespread adoption. Energy harvesting technologies bring an opportunity to address this issue by enabling the development of self-powered BSNs. However, the dynamic nature of energy harvesting sources poses a challenge to self-powered vigilance. In this paper, an application-driven dynamic power management (DPM) method for self-powered BSNs is presented that optimally adapts system operation to energy availability while meeting application requirements for vigilant monitoring. Vigilant Atrial Fibrillation (AF) monitoring is investigated as an example case study, and a simulation using real-world energy harvesting profiles is executed to validate the model and the optimal solution.

Keywords: Energy harvesting, Power optimization, Everyday health status
Abstract: This paper proposes a whole body human power-based energy harvesting (HPBEH) scheme which aggregates the power from multiple HPBEH devices distributed on a special cloth embroidered with conductive threads. Each HPBEH devices is connected by using a special connector consisting of a tack and a clutch without one-to-one wiring. In a conventional HPBEH system, each device individually has its own HPBEH element. Power aggregation system using a multiple HPBEH devices can supply higher power than conventional EH systems to a power-hungry device. And each HPBEH devices has a flexible piezoelectric element (PE) placed in the whole body joints and sole. In the flexible PEs, the output voltage and the time when output reaches the peak are different. The experimental results demonstrate the feasibility of the power aggregation system in those case.