Xavier Rafael-Palou

Improving Activity Monitoring Through a Hierarchical Approach.

Performance of sensor-based telemonitoring and home support systems depends, among other characteristics, on the reliability of the adopted sensors. Although binary sensors are quite used in the literature and also in commercial solutions to identify user’s activities, they are prone to noise and errors. In this paper, we present a hierarchical approach, based on machine learning techniques, aimed at reducing error from the sensors. The proposed approach is aimed at improving the classification accuracy in detecting if a user is at home, away, alone or with some visits. It has been integrated in a sensor-based telemonitoring and home support system. Results show an overall improvement of 15% in accuracy with respect to a rule-based approach. The system is part of the BackHome project and is currently running in 2-healthy-users’ home in Barcelona and in 3-end-users’ home in Belfast.

A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes

Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis.

Monitoring and Supporting People that Need Assistance: The BackHome Experience

People that need assistance, as for instance elderly or disabled people, may be affected by a decline in daily functioning that usually involves the reduction and discontinuity in daily routines and a worsening in the overall quality of life. Thus, there is the need to intelligent systems able to monitor indoor and outdoor activities of users to detect emergencies, recognize activities, send notifications, and provide a summary of all the relevant information. In this chapter, we present a sensor-based telemonitoring system that addresses all that issues. Its goal is twofold: (i) helping and supporting people (e.g. elderly or disabled) at home; and (ii) giving a feedback to therapists, caregivers, and relatives about the evolution of the status, behavior and habits of each monitored user. The proposed system is part of the EU project BackHome and it is currently running in three end-user’s homes in Belfast. Our experience in applying the system to monitor and assist people with severe disabilities is illustrated.

Brain–Computer Interfaces on Track to Home: Results of the Evaluation at Disabled End-Users’ Homes and Lessons Learnt.

The BackHome system is a multi-functional BCI system, the final outcome of a User Centred Design approach, whose ambition is to move BCI systems from laboratories into the home of people in need for their independent home use. The paper presents the results of testing and evaluation of the BackHome system with end-users at their own homes. Results show moderate to good acceptance from end-users, caregivers and therapists; which reported promising usability levels, good user satisfaction and levels of control in the use of services and home support based on remote monitoring tools.

Experimenting quality of life telemonitoring in a real scenario

In the last decades, the worldwide growth and adoption of eHealth solutions has impacted life expectancy and improved quality of life, especially of people living in developed countries. One key common feature of all those novel eHealth solutions is telemonitoring, which makes possible to remotely assess health status and quality of life of individuals. Telemonitoring systemsusually acquire heterogeneous data coming from sensors (physiological, biometric, environmental; wearable, non-invasive, adaptive and transparent to user) and other sources ( e.g. , interaction with the user through digital services). By analyzing thosedata, systems become aware of user context and are able to automatically infer user’s behavior as well as detect anomalies. In that way, they provide elaborated and smart knowledge to clinicians, therapists, carers, families, and the patients themselves. In this paper, we present a solution aimed at automatically assessing quality of life of people. The goal is twofold: to provide support to people in need of assistance and to inform therapists, carers and families about the improvement/worsening of qualityof life of monitored people. The paper presents first experiments that have been performed in Barcelona to automatically assess MOBILITY, SLEEPING and MOOD of a body-abled user. Since results show that the approach is effective in that scenario, thesystem has been then installed and it is currently running at three homes of people with severe disabilities.

Remotely Supporting Patients with Obstructive Sleep Apnea at Home

People suffering Obstructive Sleep Apnea are normally treated by using a device that provides continuous positive airway pressure. Currently solutions do not rely on any remote assistance and data gathered from that device are accessible to clinicians only when the patient goes to the annual visit. In this paper, we propose an IoT-based system that sends data to the cloud where are analyzed to support patients with Obstructive Sleep Apnea giving also a suitable feedback to lung specialists. The work is part of the Spanish project myOSA. Clinical trials with patients from the Hospital Arnau i Vilanova in Lleida (Spain) started on July 2016 and will last 6 months.

Sleeping recognition to assist elderly people at home

In elderly care, activities of daily living are used to assess cognitive and physical capabilities of elderly people. In fact, cognitive and physical decline may start with problems in doing daily living activities. Elderly people may not be able to complete an activity by themselves or the activity takes more time than usual. Moreover, forgetting to do some daily activity may indicate diseases that affect memory. Sleep disorders represent a very common problem for elderly people and may influence the overall quality of life. In this paper, we focus on sleeping activity and propose a study aimed at recognize this kind of activities. The goal is twofold; on the one hand, elderly people are monitored at their home having assistance in case of needs; on the other hand, therapists, caregivers, and familiars become aware of the health status of the monitored elderly people and receive alarms and alerts in case anomalies are detected. Experiments, performed with volunteers at their homes, show that the proposed approach is able to recognize sleeping activities with high accuracy.

Home-Based Activity Monitoring of Elderly People Through a Hierarchical Approach

People that need assistance, as for instance elderly or disabled people, may be affected by a decline in daily functioning that usually involves the reduction and discontinuity in daily routines and a worsening in the overall quality of life. Thus, there is the need to intelligent systems able to monitor indoor and outdoor activities of users to detect emergencies, recognize activities, send notifications, and provide a summary of all the relevant information. To this end, several sensor-based telemonitoring and home support systems have been presented in the literature. Unfortunately, performance of those systems depends, among other characteristics, on the reliability of the adopted sensors. Although binary sensors are quite used in the literature and also in commercial solutions to identify user’s activities, they are prone to noise and errors. In this chapter, we present a hierarchical approach, based on machine learning techniques, aimed at reducing errors from the sensors. The proposed approach is aimed at improving the classification accuracy in detecting if a user is at home, away, alone or with some visits. It has been integrated in a sensor-based telemonitoring and home support system. After being evaluated with a control user, the overall system has been installed in 8 elderly people’s homes in Barcelona, results are presented in this chapter.

Comparative analysis of predictive methods for early assessment of compliance with continuous positive airway pressure therapy

BackgroundPatients suffering obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with serious consequences for the patients’ health. Unfortunately, there is a clear lack of clinical analytical tools to support the early prediction of compliant patients.MethodsThis work intends to take a further step in this direction by building compliance classifiers with CPAP therapy at three different moments of the patient follow-up, before the therapy starts (baseline) and at months 1 and 3 after the baseline.ResultsResults of the clinical trial shows that month 3 was the time-point with the most accurate classifier reaching an f1-score of 87% and 84% in cross-validation and test. At month 1, performances were almost as high as in month 3 with 82% and 84% of f1-score. At baseline, where no information of patients’ CPAP use was given yet, the best classifier achieved 73% and 76% of f1-score in cross-validation and test set respectively. Subsequent analyzes carried out with the best classifiers of each time point revealed baseline factors (i.e. headaches, psychological symptoms, arterial hypertension and EuroQol visual analog scale) closely related to the prediction of compliance independently of the time-point. In addition, among the variables taken only during the follow-up of the patients, Epworth and the average nighttime hours were the most important to predict compliance with CPAP.ConclusionsBest classifiers reported high performances after one month of treatment, being the third month when significant differences were achieved with respect to the baseline. Four baseline variables were reported relevant for the prediction of compliance with CPAP at each time-point. Two characteristics more were also highlighted for the prediction of compliance at months 1 and 3.Trial registrationClinicalTrials.gov Identifier, NCT03116958. Retrospectively registered on 17 April 2017.

Towards an Intelligent Monitoring System for Patients with Obstrusive Sleep Apnea

Due to the growing incidence of chronic diseases and aging populations, the pressure to control costs and the expectations of continuous improvements in the quality of service have increased the need to understand how healthcare is provided and to determine whether cost-effective improvements to care practices can be made. In the case of people suffering Obstructive Sleep Apnea, patients using self-administer nasal Continuous Positive Airway Pressure (CPAP) may receive information on the treatment only once they go to a visit with the lung specialist. In this paper, we propose an IoT-based Intelligent Monitoring System that relies on machine learning to achieve a threefold goal: (1) it is aimed at early detecting compliance in order to predict CPAP usage; (2) it monitors the actual adherence degree to the treatment to keep informed both the patient and the lung specialists; and (3) it sends recommendations to the patient to empower her/him and to better follow up. Received on 06 December 2017; accepted on 14 December 2017; published on 19 December 2017