Towards remote monitoring and remotely supervised training

The growing number of elderly and people with chronic disorders in our western society puts such pressure on our healthcare system that innovative approaches are demanded to make our health care more effective and more efficient. One way of innovation of healthcare can be obtained by introducing new services which enable less pressure on the intramural health care and support a more independent living and self efficacy of patients. Two of such services are Remote monitoring and remotely supervised training (RMT). Remote monitoring enables freedom to the patient with the assurance that assistance is possible whenever required. Remotely supervised treatment enables efficient and effective user-centred training anywhere and anytime with an intensity not feasible in an intramural setting. It is our vision that remote monitoring and remotely supervised treatment applications will become very important for patients (safety, more in control, convenience), health care insurances (efficiency, cost reduction) and healthcare service providers (more effective, innovative)

Extending remote patient monitoring with mobile real time clinical decision support

Large scale implementation of telemedicine services such as telemonitoring and teletreatment will generate huge amounts of clinical data. Even small amounts of data from continuous patient monitoring cannot be scrutinised in real time and round the clock by health professionals. In future huge volumes of such data will have to be routinely screened by intelligent software systems. We investigate how to make m-health systems for ambulatory care more intelligent by applying a Decision Support approach in the analysis and interpretation of biosignal data and to support adherence to evidence-based best practice such as is expressed in treatment protocols and clinical practice guidelines. The resulting Clinical Decision Support Systems must be able to accept and interpret real time streaming biosignals and context data as well as the patient’s (relatively less dynamic) clinical and administrative data. In this position paper we describe the telemonitoring/teletreatment system developed at the University of Twente, based on Body Area Network (BAN) technology, and present our vision of how BAN-based telemedicine services can be enhanced by incorporating mobile real time Clinical Decision Support. We believe that the main innovative aspects of the vision relate to the implementation of decision support on a mobile platform; incorporation of real time input and analysis of streaming\ud biosignals into the inferencing process; implementation of decision support in a distributed system; and the consequent challenges such as maintenance of consistency of knowledge, state and beliefs across a distributed environment

A serious game for COPD patients to perform physiotherapeutic exercises

The goal of this research was 1) to investigate the usability of the Orange Submarine game, and 2) to explore the changes in saturation and pulse rate in COPD patients while playing the game. The game was positively received by the patients and could provide a new fun way for performing exercises, either at home or as part of the regular treatment

The predictive value of pain event-related potentials for the clinical experience of pain

Event-related potentials (ERPs) have been found to be related to subjective experience of experimental pain. But how are they related to the subjective experience of clinical pain? The current study investigated the predictive value of the pain ERP for the subjective experience of clinical pain. Event-related potentials in response to experimental pain were measured in 75 chronic low back pain sufferers. In addition, a two-week registration to note the amount of pain they experienced in daily life was done. The results demonstrate that the N2-component at Cz and C4 of the pain ERP (contralateral to the side of the stimulation) were significant predictors of clinical pain, and even stronger predictors than the accompanying subjective ratings of experimental pain. Thus, it seems promising to use event-related potentials as a more objective measure to make predictions about a person's likely pain experience in daily life

Fine-tuning a context-aware system application by using user-centred design methods

Context-Aware Systems in the home environment can provide an effective solution for supporting wellbeing and autonomy for the elderly. The definition and implementation of the system architecture for a particular assisted living healthcare application entail both technological and usability challenges. If issues regarding users’ concerns and desires are taken into account in the early stages of the system development users can benefit substantially more from this technology. In this paper, we describe our initial experiences with different user-centred design methods, as they are applied in the process of fine-tuning a context-aware system architecture to improve quality of life for elderly THR patients (Total Hip Replacement). The insights resulting from this approach result in a clearer functional specification towards a better fit with the user needs regarding information need of the patient as well as the physiotherapist. Important system requirements as timing and content of the feedback are much more fruitful in an earlier phase of the development process. User-centred design methods help to better understand the needed functional features of a context-aware system, thereby saving time and helping developers to improve adoption of the system by the users

Personalised mobile services supporting the implementation of clinical guidelines

Telemonitoring is emerging as a compelling application of Body Area Networks (BANs). We describe two health BAN systems developed respectively by a European team and an Australian team and discuss some issues encountered relating to formalization of clinical knowledge to support real-time analysis and interpretation of BAN data. Our example application is an evidence-based telemonitoring and teletreatment application for home-based rehabilitation. The application is intended to support implementation of a clinical guideline for cardiac rehabilitation following myocardial infarction. In addition to this the proposal is to establish the patient’s individual baseline risk profile and, by real-time analysis of BAN data, continually re-assess the current risk level in order to give timely personalised feedback. Static and dynamic risk factors are derived from literature. Many sources express evidence probabilistically, suggesting a requirement for reasoning with uncertainty; elsewhere evidence requires qualitative reasoning: both familiar modes of reasoning in KBSs. However even at this knowledge acquisition stage some issues arise concerning how best to apply the clinical evidence. Furthermore, in cases where insufficient clinical evidence is currently available, telemonitoring can yield large collections of clinical data with the potential for data mining in order to furnish more statistically powerful and accurate clinical evidence

Motor unit properties of biceps brachii in chronic stroke patients assessed with high-density surface EMG

The aim of this study was to investigate motor unit (MU) characteristics of the biceps brachii in post-stroke patients, using high-density surface electromyography (sEMG). Eighteen chronic hemiparetic stroke patients took part. The Fugl-Meyer score for the upper extremity was assessed. Subjects performed an isometric step contraction consisting of force levels from 5 to 50% MVC while sEMG of the biceps brachii was recorded with a two dimensional 16-channel electrode array. This was repeated for both sides. Motor unit action potentials (MUAPs) were extracted from the EMG signals, and their root-mean-square value (RMSMUAP, reflecting MU size) and mean frequency of the power spectrum (FMEANMUAP, reflecting recruitment threshold) were calculated. \ud FMEANMUAP was smaller on the affected than on the unaffected side, indicating an increased contribution of low-threshold motor units, possibly related to degeneration of high-threshold motor units. The ratio of RMSMUAP on the affected side divided by that on the unaffected side correlated significantly with the Fugl-Meyer score. This ratio may reflect the extent to which reinnervation has occurred on the affected side. \u

Universality of Fluctuation-Dissipation Ratios: The Ferromagnetic Model

We calculate analytically the fluctuation-dissipation ratio (FDR) for Ising ferromagnets quenched to criticality, both for the long-range model and its short-range analogue in the limit of large dimension. Our exact solution shows that, for both models, $X^\infty=1/2$ if the system is unmagnetized while $X^\infty=4/5$ if the initial magnetization is non-zero. This indicates that two different classes of critical coarsening dynamics need to be distinguished depending on the initial conditions, each with its own nontrivial FDR. We also analyze the dependence of the FDR on whether local and global observables are used. These results clarify how a proper local FDR (and the corresponding effective temperature) should be defined in long-range models in order to avoid spurious inconsistencies and maintain the expected correspondence between local and global results; global observables turn out to be far more robust tools for detecting non-equilibrium FDRs.Comment: 14 pages, revtex4, published version. Changes from v1: added discussion of refs [16,36,37], other observables and local correlation/response in short-range mode

COVID-BEHAVE dataset:measuring human behaviour during the COVID-19 pandemic

Aiming to illuminate the effects of enforced confinements on people’s lives, this paper presents a novel dataset that measures human behaviour holistically and longitudinally during the COVID-19 outbreak. In particular, we conducted a study during the first wave of the lockdown, where 21 healthy subjects from the Netherlands and Greece participated, collecting multimodal raw and processed data from smartphone sensors, activity trackers, and users’ responses to digital questionnaires. The study lasted more than two months, although the duration of the data collection varies per participant. The data are publicly available and can be used to model human behaviour in a broad sense as the dataset explores physical, social, emotional, and cognitive domains. The dataset offers an exemplary perspective on a given group of people that could be considered to build new models for investigating behaviour changes as a consequence of the lockdown. Importantly, to our knowledge, this is the first dataset combining passive sensing, experience sampling, and virtual assistants to study human behaviour dynamics in a prolonged lockdown situation