In the ActivAbles and STARR projects we have developed interactive training tools for stroke survivors. A prototype system includes an interactive balance foam pad, feedback lamps and a step counting game app which all connect to a central server. The feedback is designed to be inclusive - designs are multimodal, and the setup is flexible and can easily be adapted. We describe the system and report results from a feasibility study with 10 stroke survivors who used the system for a longer period in the home.

Wheezing is one of the most prominent symptoms for pulmonary attack. Hence, wheezing detection has attracted a lot of attention in recent years. However, there is a dearth of a reliable method that can automatically detect wheezing events during each respiration phase in presence of several concurrent sounds such as cough, throat clearing, and nasal breathing. In this paper, we develop a model called WheezeD which, to the best of our knowledge, represents the first step towards developing a computational model for respiration phased based wheeze detection. WheezeD has two components, first, we develop an algorithm to detect respiration phase from audio data. We, then transform the audio into 2-D spectro-temporal image and develop a convolutional neural network (CNN) based wheeze detection model. We evaluate the model performance and compare them to conventional approaches. Experiments on a public dataset show that our model can identify wheezing event with an accuracy of 96.99%, specificity of 97.96%, and sensitivity of 96.08%, which is over 10% improvement in performance compared to the best accuracy reported in the literature by using traditional machine learning models on the same dataset. Moreover, we discuss how WheezeD may be used towards assessment and computation of patient severity.

Stair climbing is a physical activity that can easily be performed in daily life and has a positive influence on health. We investigated the role of technology to promote stair climbing by designing and implementing two prototypes in real-life circumstances. To understand user acceptance and interaction we conducted user studies in a shopping mall and a university building. Based on our findings we derived lessons learned for designing future technology for the promotion of stair climbing.

In this poster, we present a pilot study on the efficacy in adherence by adding social components into LumaPath, an Immersive Virtual Environment (IVE) built for motivating chronic pain patients to perform range of motion (RoM) exercises, especially aging patients who live with Rheumatoid Arthritis (RA) and Osteoarthritis (OA). Based on results from this pilot study as well as from prior work, we argue that adding a certain level of social presence for patients in an IVE such as LumaPath may positively influence their adherence to using assistive tools for treating arthritis and chronic pain.

Though many new technologies have been adopted for stress detection, communicating stress to the users is still experienced primarily through visual or auditory channels. However, these commonly used feedback channels are already associated with smartphone notifications. Instead, we focus on thermal feedback yielding the advantage to preserve privacy due to its unobtrusiveness. By this work, we contribute an investigation of thermal feedback for notifying users about stress comprising the exploration of the preferred temperature level, rate of change, and body location. Accordingly, we compared different stimuli for each for those in a user study involving 21 participants. From their quantitative and qualitative feedback, we found that cold stimuli are preferred in general, showing that ± − 0.5◦C is the optimal rate of change and preferably when presented at the lower back.We conclude with discussing our findings from both the quantitative and qualitative data and finally present our research agenda paving the way for thermal feedback as a stress notifier.

The existing technologies used to capture Electrocardiograms (ECGs) pose a number of challenges, including bulky hardware, slow processing times, and inadequate built-in support for ensuring the quality of collected data. We worked with a major healthcare organization to develop mobile, tablet-based ECG hardware and software. In this work, we describe requirements gathering, design, development activities for our mobile ECG data collection solutions, followed by an initial user testing. The results of this research show the potential of mobile device-based ECGs in the clinical domain, and point to types of automation that the healthcare industry may resist and a need for care in automating the collection of ECGs.

Good sleep is a key component of good health, and as such, how to obtain quality sleep is of concern to many people. Circadian rhythms vary between individuals and play an important role in regulating sleep, however, they are currently not monitored by commercially available wearables. Previous work has shown that circadian rhythm is reflected in changes wrist temperature. In this work, we present a prototype wristband that measures motion and temperature at the wrist. We developed an algorithm to detect wrist temperature increase onset, which is an indicator of the body preparing for sleep. Our results demonstrate that our algorithm is able to detect wrist temperature increase onset, which appears to occur at the same time for the same person. We also show that temperature increase onset varies between people as does overall temperature patterns between people. The detection of wrist temperature patterns gives us a deeper understanding of the mechanisms underlying sleep and could be a valuable component of a personalized sleep monitoring algorithm.

The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socio-economic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H2O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV).

In recent years, many revocable group signatures schemes were proposed; however, the backward security, which can disable a revoked signer to generate group signatures pertaining to future time periods, was not fully realized through those schemes. In this paper, we present a security model with the definition of backward security and propose a revocable group signatures scheme that is more efficient than previous ones, especially in Sign and Verify algorithms, which are performed much more frequently than others. In addition, considering the heavy workload of group manager in original group signatures, we separate a group into groups by employing a decentralized model to make our scheme more scalable, and thus more practical in real-life applications.

Due to the multi-scale fusion of cyber-physical systems, attackers can attack the physical space based on cyber space intentionally. This process can cause cascading failures and then in sharp contrast with the previous physical space. Thus, how to effectively evaluate the security of cyber-physical systems becomes critical. In this paper, we model the cyber-physical systems and then analyze the cascading failure process under target attack strategy. After doing that, based on the comparative analysis of simulation experiments, we analyze the main factors affecting the security of the cyber-physical system.