Technology in Parkinson's disease:challenges and opportunities

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society

Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson\u27s Disease Patients

Parkinson\u27s Disease (PD) affects over 1% of the population over 60 years of age and is expected to reach 1 million in the USA by the year 2020, growing by 60 thousand each year. It is well understood that PD is characterized by dopaminergic loss, leading to decreased executive function causing motor symptoms such as tremors, bradykinesia, dyskinesia, and freezing of gait (FoG) as well as non-motor symptoms such as loss of smell, depression, and sleep abnormalities. A PD diagnosis is difficult to make since there is no worldwide approved test and difficult to manage since its manifestations are widely heterogeneous among subjects. Thus, understanding the patient subsets and the neural biomarkers that set them apart will lead to improved personalized care. To explore the physiological alternations caused by PD on neurological pathways and their effect on motor control, it is necessary to detect the neural activity and its dissociation with healthy physiological function. To this effect, this study presents a custom ultra-wearable sensor solution, consisting of electroencephalograph, electromyograph, ground reaction force, and symptom measurement sensors for the exploration of neural biomarkers during active gait paradigms. Additionally, this study employed novel de-noising techniques for dealing with the motion artifacts associated with active gait EEG recordings and compared time-frequency features between a group of PD with FoG and a group of age-matched controls and found significant differences between several EEG frequency bands during start and end of normal walking (with a p\u3c0.05)

Wearables for independent living in older adults: Gait and falls

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised

A Review of Different Applications of Wireless Sensor Network (WSN) in Monitoring Rehabilitation

Parkinson’s disease is a neurodegenerative brain disorder that affects movement. The lack of dopamine in the brain cells causes patients have lesser ability to regulate movement and emotions as time goes on. There is no cure for this disease. Although drug therapies are successful for some patients, most of the patients usually develop motor complications. In this paper, we presented our work towards the comparison of several wireless sensor network (WSN) systems for monitoring Parkinson’s patients. The designs of each system are explored. The parts being considered to design a wireless sensor network and limitations are discussed. These findings helped us to suggest a possible wireless sensor network system to supervise Parkinson’s diseases patients for a more extended period of time

Designing socially acceptable mHealth technologies for Parkinson's disease self-management

Mobile health (mHealth) technologies for Parkinson’s disease management have developed quickly in recent years. Research in this area typically focuses on evaluation of the accuracy and reliability of the technology, often to the exclusion of social factors and patient perspectives. This qualitative systematic review aimed to investigate the barriers to and facilitators of use mHealth technologies for disease self-management from the perspective of People with Parkinson's (PwP). Findings revealed that technological, as well as social, and financial factors are key considerations for mHealth design, to ensure its acceptability, and long-term use by PwP. This study proposes that a co-design approach could contribute to the design and development of mHealth that are socially acceptable to PwP, and enable their successful long-term use in the context of daily life.Mobile health (mHealth) technologies for Parkinson’s disease management have developed quickly in recent years. Research in this area typically focuses on evaluation of the accuracy and reliability of the technology, often to the exclusion of social factors and patient perspectives. This qualitative systematic review aimed to investigate the barriers to and facilitators of use mHealth technologies for disease self-management from the perspective of People with Parkinson's (PwP). Findings revealed that technological, as well as social, and financial factors are key considerations for mHealth design, to ensure its acceptability, and long-term use by PwP. This study proposes that a co-design approach could contribute to the design and development of mHealth that are socially acceptable to PwP, and enable their successful long-term use in the context of daily life

Free-living monitoring of Parkinson’s disease: lessons from the field

Wearable technology comprises miniaturized sensors (e.g. accelerometers) worn on the body and/or paired with mobile devices (e.g. smart phones) allowing continuous patient monitoring in unsupervised, habitual environments (termed free-living). Wearable technologies are revolutionising approaches to healthcare due to their utility, accessibility and affordability. They are positioned to transform Parkinson’s disease (PD) management through provision of individualised, comprehensive, and representative data. This is particularly relevant in PD where symptoms are often triggered by task and free-living environmental challenges that cannot be replicated with sufficient veracity elsewhere. This review concerns use of wearable technology in free-living environments for people with PD. It outlines the potential advantages of wearable technologies and evidence for these to accurately detect and measure clinically relevant features including motor symptoms, falls risk, freezing of gait, gait, functional mobility and physical activity. Technological limitations and challenges are highlighted and advances concerning broader aspects are discussed. Recommendations to overcome key challenges are made. To date there is no fully validated system to monitor clinical features or activities in free living environments. Robust accuracy and validity metrics for some features have been reported, and wearable technology may be used in these cases with a degree of confidence. Utility and acceptability appears reasonable, although testing has largely been informal. Key recommendations include adopting a multi-disciplinary approach for standardising definitions, protocols and outcomes. Robust validation of developed algorithms and sensor-based metrics is required along with testing of utility. These advances are required before widespread clinical adoption of wearable technology can be realise

Future Opportunities for IoT to Support People with Parkinson’s

Recent years have seen an explosion of internet of things (IoT) technologies being released to the market. There has also been an emerging interest in the potentials of IoT devices to support people with chronic health conditions. In this paper, we describe the results of engagements to scope the future potentials of IoT for supporting people with Parkinson’s. We ran a 2-day multi-disciplinary event with professionals with expertise in Parkinson’s and IoT, to explore the opportunities, challenges and benefits. We then ran 4 workshops, engaging 13 people with Parkinson’s and caregivers, to scope out the needs, values and desires that the community has for utilizing IoT to monitor their symptoms. This work contributes a set of considerations for future IoT solutions that might support people with Parkinson’s in better understanding their condition, through the provision of objective measurements that correspond to their, currently unmeasured, subjective experiences

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI) : Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.Peer reviewe

Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device

Objective: Freezing of Gait (FOG) often described as the sensation of “the feet being glued to the ground” is prevalent in people with Parkinson's disease (PD) and severely disturbs mobility. In addition to tracking disease progression, precise detection of the exact boundaries for each FOG episode may enable new technologies capable of “breaking” FOG in real time. This study investigates the limits of sensitivity and performance for automatic device-based FOG detection. Methods: Eight machine-learning classifiers (including Neural Networks, Ensemble & Support Vector Machine) were developed using (i) accelerometer and (ii) accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed a walking and mobility task designed to elicit FOG. Two clinicians independently annotated the precise FOG episodes using synchronized video according to international guidelines, which were incorporated into a flowchart algorithm developed for this study. Device-detected FOG episodes were compared to the annotated FOG episodes using 10-fold cross-validation to determine accuracy and with Interclass Correlation Coefficients (ICC) to assess level of agreement. Results: Development used 50,962 windows of data representing over 10 hours of data and annotated activities. Very strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity and ICC 1,1 = 0.97 for total FOG duration). Device-based performance varied by method, complexity and cost matrix. The Neural Network that used only 67 accelerometer features provided a good balance between high sensitivity to FOG (89% sensitivity, 81% specificity and ICC 1,1 = 0.83) and solution stability (validation loss ≤ 5%). Conclusion: The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The superior agreement between clinicians indicates there is room to improve future device-based FOG detection by using larger and more varied data sets. Significance: This study has clinical implications with regard to improving PD care by reducing reliance on clinical FOG assessments and time-consuming visual inspection. It shows high sensitivity to automatically detect FOG is possible