A telehealth system for Parkinson's disease remote monitoring. The PERFORM approach

This paper summarizes the experience and the lessons learned from the European project PERFORM (A sophisticated multi-parametric system FOR the continuous effective assessment and monitoring of motor status in Parkinson s disease and other neurodegenerative diseases). PERFORM is aimed to provide a telehealth system for the remote monitoring of Parkinson s disease patients (PD) at their homes. This paper explains the global experience with PERFORM. It summarizes the technical performance of the system and the feedback received from the patients in terms of usability and wearability

FIT A Fog Computing Device for Speech TeleTreatments

There is an increasing demand for smart fogcomputing gateways as the size of cloud data is growing. This paper presents a Fog computing interface (FIT) for processing clinical speech data. FIT builds upon our previous work on EchoWear, a wearable technology that validated the use of smartwatches for collecting clinical speech data from patients with Parkinson's disease (PD). The fog interface is a low-power embedded system that acts as a smart interface between the smartwatch and the cloud. It collects, stores, and processes the speech data before sending speech features to secure cloud storage. We developed and validated a working prototype of FIT that enabled remote processing of clinical speech data to get speech clinical features such as loudness, short-time energy, zero-crossing rate, and spectral centroid. We used speech data from six patients with PD in their homes for validating FIT. Our results showed the efficacy of FIT as a Fog interface to translate the clinical speech processing chain (CLIP) from a cloud-based backend to a fog-based smart gateway.Comment: 3 pages, 5 figures, 1 table, 2nd IEEE International Conference on Smart Computing SMARTCOMP 2016, Missouri, USA, 201

Coordinated Speech Therapy, Physiotherapy, and Pharmaceutical Care Telehealth for People with Parkinson Disease in Rural Communities: An Exploratory, 8-Week Cohort Study for Feasibility, Safety, and Signal of Efficacy

Introduction: The potential for coordinated, multidisciplinary telehealth to help connect people with Parkinson disease (PD) in rural areas to PD specialists is crucial in optimizing care. Therefore, this study aimed to test the feasibility, safety, and signal of efficacy of a coordinated telehealth program, consisting of speech therapy, physiotherapy, and pharmaceutical care, for people with PD living in some rural US communities. Methods: Fifteen individuals with PD living in rural Wyoming and Nevada, USA, participated in this single-cohort, 8-week pilot study. Participants were assessed before and after 8 weeks of coordinated, one-on-one telehealth using the following outcomes: (1) feasibility: session attendance and withdrawal rate; (2) safety: adverse events; and (3) signal of efficacy: Communication Effectiveness Survey, acoustic data (intensity, duration, work (intensity times duration)), Parkinson’s Fatigue Scale, 30 second Sit-to-Stand test, Parkinson’s Disease Questionnaire – 39, Movement Disorder Society Unified Parkinson’s Disease Rating Scale – Part III, and medication adherence. Results: Average attendance was greater than 85% for all participants. There were no serious adverse events and only nine minor events during treatment sessions (0.9% of all treatment sessions had a participant report of an adverse event); all nine cases resolved without medical attention. Although 14 of 16 outcomes had effect sizes trending in the direction of improvement, only two were statistically significant using non-parametric analyses: 30 second Sit-to-Stand (pre-test median=11.0 (interquartile range (IQR)=6.0); post-test median=12.0 (IQR=3.0) and acoustic data work (pre-test median=756.0 dB s (IQR=198.4); post-test median=876.3 dB s (IQR=455.5), p \u3c 0.05. Conclusion: A coordinated, multidisciplinary telehealth program was safe and feasible for people in rural communities who have PD. This telehealth program also yielded a signal of efficacy for most of the outcomes measured in the study

Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities

Research and development work relating to assistive technology 2010-11 (Department of Health) Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197

Fog Data: Enhancing Telehealth Big Data Through Fog Computing

The size of multi-modal, heterogeneous data collected through various sensors is growing exponentially. It demands intelligent data reduction, data mining and analytics at edge devices. Data compression can reduce the network bandwidth and transmission power consumed by edge devices. This paper proposes, validates and evaluates Fog Data, a service-oriented architecture for Fog computing. The center piece of the proposed architecture is a low power embedded computer that carries out data mining and data analytics on raw data collected from various wearable sensors used for telehealth applications. The embedded computer collects the sensed data as time series, analyzes it, and finds similar patterns present. Patterns are stored, and unique patterns are transmited. Also, the embedded computer extracts clinically relevant information that is sent to the cloud. A working prototype of the proposed architecture was built and used to carry out case studies on telehealth big data applications. Specifically, our case studies used the data from the sensors worn by patients with either speech motor disorders or cardiovascular problems. We implemented and evaluated both generic and application specific data mining techniques to show orders of magnitude data reduction and hence transmission power savings. Quantitative evaluations were conducted for comparing various data mining techniques and standard data compression techniques. The obtained results showed substantial improvement in system efficiency using the Fog Data architecture

Predictive data analytics in telecare and telehealth : systematic scoping review

Background: Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care. Objective: This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings. Methods: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O’Malley’s methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria. Results: In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth. Conclusions: All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested

Feasibility study of a wearable system based on a wireless body area network for gait assessment in Parkinson's disease patients

Parkinson’s disease (PD) alters the motor performance of affected individuals. The dopaminergic denervation of the striatum, due to substantia nigra neuronal loss, compromises the speed, the automatism and smoothness of movements of PD patients. The development of a reliable tool for long-term monitoring of PD symptoms would allow the accurate assessment of the clinical status during the different PD stages and the evaluation of motor complications. Furthermore, it would be very useful both for routine clinical care as well as for testing novel therapies. Within this context we have validated the feasibility of using a Body Network Area (BAN) of wireless accelerometers to perform continuous at home gait monitoring of PD patients. The analysis addresses the assessment of the system performance working in real environments

Assessing the Benefits of a Teleassessment Solution Using a FVM Perspective

The recent COVID-19 pandemic has served to highlight the benefits of digital health in general and telehealth in particular. One area of telehealth that is particularly important is that of teleassessment. Currently, we are witnessing an exponential growth in total knee and total hip replacements (TKR) (THR) due to an aging population coupled with longer life expectancy which is leading to a high likelihood of an unsustainable burden for healthcare delivery in Australia. To address this imminent challenge, the following proffers a tele-assessment solution, ARIADNE (Assist foR hIp AnD kNEe), that can provide high quality care, with access for all and support for high value outcomes. A fit viability assessment is provided to demonstrate benefits of the proffered solution