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

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

Contactless finger tapping detection at C band

The rapid finger tap test is widely used in clinical assessment of dyskinesias in Parkinson’s disease. In clinical practice, doctors rely on their clinical experience and use the Parkinson’s Disease Uniform Rating Scale to make a brief judgment of symptoms. We propose a novel C-band microwave sensing method to evaluate finger tapping quantitatively and qualitatively in a non-contact way based on wireless channel information (WCI). The phase difference between adjacent antennas is used to calibrate the original random phase. Outlier filtering and smoothing filtering are used to process WCI waveforms. Based on the resulting signal, we define and extract a set of features related to the features described in UPDRS. Finally, the features are input into a support vector machine (SVM) to obtain results for patients with different severity. The results show that the proposed system can achieve an average accuracy of 99%. Compared with the amplitude, the average quantization accuracy of the phase difference on finger tapping is improved by 3%. In the future, the proposed system could assist doctors to quantify the movement disorders of patients, and it is very promising to be a candidate for clinical practice

Motion Capture System for Finger Movement Measurement in Parkinson Disease

Parkinson’s disease (PD) is a chronic neurodegenerative disorder that affects almost 1% of the population in the age group above 60 years. The key symptom in PD is the restriction of mobility. The progress of PD is typically documented using the Unified Parkinson’s Disease Rating Scale (UPDRS), which includes a finger-tapping test. We created a measurement tool and a methodology for the objective measurement of the finger-tapping test. We built a contactless three-dimensional (3D) capture system using two cameras and light-passive (wireless) reflexive markers. We proposed and implemented an algorithm for extracting, matching, and tracing markers. The system provides the 3D position of spherical or hemispherical markers in real time. The system’s functionality was verified with the commercial motion capture system OptiTrack. Our motion capture system is easy to use, saves space, is transportable, and needs only a personal computer for data processing—the ideal solution for an outpatient clinic. Its features were successfully tested on 22 patients with PD and 22 healthy control subjects

A Proposal for New Algorithm that Defines Gait-Induced Acceleration and Gait Cycle in Daily Parkinsonian Gait Disorders

We developed a new device, the portable gait rhythmogram (PGR), to record up to 70 hrs of movement-induced accelerations. Acceleration values induced by various movements, averaged every 10 min, showed gamma distribution, and the mean value of this distribution was used as an index of the amount of overall movements. Furthermore, the PGR algorithm can specify gait-induced accelerations using the pattern-matching method. Analysis of the relationship between gait-induced accelerations and gait cycle duration makes it possible to quantify Parkinson’s disease (PD)-specific pathophysiological mechanisms underlying gait disorders. Patients with PD showed the following disease-specific patterns: (1) reduced amount of overall movements and (2) low amplitude of gait-induced accelerations in the early stages of the disease, which was compensated by fast stepping. Loss of compensation was associated with slow stepping gait, (3) narrow range of gait-induced acceleration amplitude and gait cycle duration, suggesting monotony, and (4) evident motor fluctuations during the day by tracing changes in the above two parameters. Prominent motor fluctuation was associated with frequent switching between slow stepping mode and active mode. These findings suggest that monitoring various movement- and gait-induced accelerations allows the detection of specific changes in PD. We conclude that continuous long-term monitoring of these parameters can provide accurate quantitative assessment of parkinsonian clinical motor signs

An Evaluation of KELVIN, an Artificial Intelligence Platform, as an Objective Assessment of the MDS UPDRS Part III

BACKGROUND: Parkinson's disease severity is typically measured using the Movement Disorder Society Unified Parkinson's disease rating scale (MDS-UPDRS). While training for this scale exists, users may vary in how they score a patient with the consequence of intra-rater and inter-rater variability. OBJECTIVE: In this study we explored the consistency of an artificial intelligence platform compared with traditional clinical scoring in the assessment of motor severity in PD. METHODS: Twenty-two PD patients underwent simultaneous MDS-UPDRS scoring by two experienced MDS-UPDRS raters and the two sets of accompanying video footage were also scored by an artificial intelligence video analysis platform known as KELVIN. RESULTS: KELVIN was able to produce a summary score for 7 MDS-UPDRS part 3 items with good inter-rater reliability (Intraclass Correlation Coefficient (ICC) 0.80 in the OFF-medication state, ICC 0.73 in the ON-medication state). Clinician scores had exceptionally high levels of inter-rater reliability in both the OFF (0.99) and ON (0.94) medication conditions (possibly reflecting the highly experienced team). There was an ICC of 0.84 in the OFF-medication state and 0.31 in the ON-medication state between the mean Clinician and mean Kelvin scores for the equivalent 7 motor items, possibly due to dyskinesia impacting on the KELVIN scores. CONCLUSION: We conclude that KELVIN may prove useful in the capture and scoring of multiple items of MDS-UPDRS part 3 with levels of consistency not far short of that achieved by experienced MDS-UPDRS clinical raters, and is worthy of further investigation

Technological advances in deep brain stimulation:Towards an adaptive therapy

Parkinson's disease (PD) is neurodegenerative movement disorder and a treatment method called deep brain stimulation (DBS) may considerably reduce the patient’s motor symptoms. The clinical procedure involves the implantation of a DBS lead, consisting of multiple electrode contacts, through which continuous high frequency (around 130 Hz) electric pulses are delivered in the brain. In this thesis, I presented the research which had the goal to improve current DBS technology, focusing on bringing the conventional DBS system a step closer to adaptive DBS, a personalized DBS therapy. The chapters in this thesis can be seen as individual building blocks for such an adaptive DBS system. After the general introduction, the first two chapters, two novel DBS lead designs are studied in a computational model. The model showed that both studied leads were able to exploit the novel distribution of the electrode contacts to shape and steer the stimulation field to activate more neurons in the chosen target compared to the conventional lead, and to counteract lead displacement. In the fourth chapter, an inverse current source density (CSD) method is applied on local field potentials (LFP) measured in a rat model. The pattern of CSD sources can act as a landmark within the STN to locate the potential stimulation target. The fifth and final chapter described the last building block of the DBS system. We introduced an inertial sensors and force sensor based measurement system, which can record hand kinematics and joint stiffness of PD patients. A system which can act as a feedback signal in an adaptive DBS system

Exploration of digital biomarkers in chronic low back pain and Parkinson’s disease

Chronic pain and Parkinson’s disease are illnesses with personal disease progression, symptoms, and the experience of these. The ability to measure and monitor the symptoms by digitally and remotely is still limited. The aim was to study the usability and feasibility of real-world data from wearables, mobile devices, and patients in exploring digital biomarkers in these diseases. The key hypothesis was that this allows us to measure, analyse and detect clinically valid digital signals in movement, heart rate and skin conductance data. The laboratory grade data in chronic pain were collected in an open feasibility study by using a program and built-in sensors in virtual reality devices. The real-world data were collected with a randomized clinical study by clinical assessments, built-in sensors, and two wearables. The laboratory grade dataset in Parkinson’s disease was obtained from Michael J. Fox Foundation. It contained sensor data from three wearables with clinical assessments. The real-world data were collected with a clinical study by clinical assessments, a wearable, and a mobile application. With both diseases the laboratory grade data were first explored, before the real-world data were analyzed. The classification of chronic pain patients with the laboratory grade movement data was possible with a high accuracy. A novel real-world digital signal that correlates with clinical outcomes was found in chronic low back pain patients. A model that was able to detect different movement states was developed with laboratory grade Parkinson’s disease data. A detection of these states followed by the quantification of symptoms was found to be a potential method for the future. The usability of data collection methods in both diseases were found promising. In the future the analyses of movement data in these diseases could be further researched and validated as a movement based digital biomarkers to be used as a surrogate or additional endpoint. Combining the data science with the optimal usability enables the exploitation of digital biomarkers in clinical trials and treatment.Digitaalisten biomarkkereiden tunnistaminen kroonisessä alaselkäkivussa ja Parkinsonin taudissa Krooninen kipu ja Parkinsonin tauti ovat oireiden, oirekokemuksen sekä taudin kehittymisen osalta yksilöllisiä sairauksia. Kyky mitata ja seurata oireita etänä on vielä alkeellista. Väitöskirjassa tutkittiin kaupallisten mobiili- ja älylaitteiden hyödyntämistä digitaalisten biomarkkereiden löytämisessä näissä taudeissa. Pääolettamus oli, että kaupallisten älylaitteiden avulla kyetään tunnistamaan kliinisesti hyödyllisiä digitaalisia signaaleja. Kroonisen kivun laboratorio-tasoinen data kerättiin tätä varten kehitettyä ohjelmistoa sekä kaupallisia antureita käyttäen. Reaaliaikainen kipudata kerättiin erillisen hoito-ohjelmiston tehoa ja turvallisuutta mitanneessa kliinisessä tutkimuksessa sekä kliinisiä arviointeja että anturidataa hyödyntäen. Laboratorio-tasoinena datana Parkinsonin taudissa käytettiin Michael J. Fox Foundationin kolmella eri älylaitteella ja kliinisin arvioinnein kerättyä dataa. Reaaliaikainen data kerättiin käyttäen kliinisia arviointeja, älyranneketta ja mobiilisovellusta. Molempien indikaatioiden kohdalla laboratoriodatalle tehtyä eksploratiivista analyysia hyödynnettiin itse reaaliaikaisen datan analysoinnissa. Kipupotilaiden tunnistaminen laboratorio-tasoisesta liikedatasta oli mahdollista korkealla tarkkuudella. Reaaliaikaisesta liikedatasta löytyi uusi kliinisten arviointien kanssa korreloiva digitaalinen signaali. Parkinsonin taudin datasta kehitettiin uusi liiketyyppien tunnistamiseen tarkoitettu koneoppimis-malli. Sen hyödyntäminen liikedatan liiketyyppien tunnistamisessa ennen varsinaista oireiden mittausta on lupaava menetelmä. Käytettävyys molempien tautien reaaliaikaisissa mittausmenetelmissä havaittiin toimivaksi. Reaaliaikaiseen, kaupallisin laittein kerättävään liikedataan pohjautuvat digitaaliset biomarkkerit ovat lupaava kohde jatkotutkimukselle. Uusien analyysimenetelmien yhdistäminen optimaaliseen käytettävyyteen mahdollistaa tulevaisuudessa digitaalisten biomarkkereiden hyödyntämisen sekä kroonisten tautien kliinisessä tutkimuksessa että itse hoidossa

Empowering patients in self-management of parkinson's disease through cooperative ICT systems

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved