Technical and clinical view on ambulatory assessment in Parkinson's disease

Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience

BACKGROUND

Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA).

RESEARCH QUESTION

How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled?

METHODS

The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects.

RESULTS

FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA.

SIGNIFICANCE

We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.

Exercise and gait/movement analyses in treatment and diagnosis of Parkinson's Disease

Cardinal motor symptoms in Parkinson's disease (PD) include bradykinesia, rest tremor and/or rigidity. This symptomatology can additionally encompass abnormal gait, balance and postural patterns at advanced stages of the disease. Besides pharmacological and surgical therapies, physical exercise represents an important strategy for the management of these advanced impairments. Traditionally, diagnosis and classification of such abnormalities have relied on partially subjective evaluations performed by neurologists during short and temporally scattered hospital appointments. Emerging sports medical methods, including wearable sensor-based movement assessment and computational-statistical analysis, are paving the way for more objective and systematic diagnoses in everyday life conditions. These approaches hold promise to facilitate customizing clinical trials to specific PD groups, as well as personalizing neuromodulation therapies and exercise prescriptions for each individual, remotely and regularly, according to disease progression or specific motor symptoms. We aim to summarize exercise benefits for PD with a specific emphasis on gait and balance deficits, and to provide an overview of recent advances in movement analysis approaches, notably from the sports science community, with value for diagnosis and prognosis. Although such techniques are becoming increasingly available, their standardization and optimization for clinical purposes is critically missing, especially in their translation to complex neurodegenerative disorders such as PD. We highlight the importance of integrating state-of-the-art gait and movement analysis approaches, in combination with other motor, electrophysiological or neural biomarkers, to improve the understanding of the diversity of PD phenotypes, their response to therapies and the dynamics of their disease progression.

Can Gait Features Help in Differentiating Parkinson's Disease Medication States and Severity Levels? A Machine Learning Approach

Parkinson's disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.

Gait Analysis with Wearables Is a Potential Progression Marker in Parkinson's Disease

Gait disturbance is a prototypical feature of Parkinson's disease (PD), and the quantification of gait using wearable sensors is promising. This study aimed to identify gait impairment in the early and progressive stages of PD according to the Hoehn and Yahr (H-Y) scale. A total of 138 PD patients and 56 healthy controls (HCs) were included in our research. We collected gait parameters using the JiBuEn gait-analysis system. For spatiotemporal gait parameters and kinematic gait parameters, we observed significant differences in stride length (SL), gait velocity, the variability of SL, heel strike angle, and the range of motion (ROM) of the ankle, knee, and hip joints between HCs and PD patients in H-Y Ⅰ-Ⅱ. The changes worsened with the progression of PD. The differences in the asymmetry index of the SL and ROM of the hip were found between HCs and patients in H-Y Ⅳ. Additionally, these gait parameters were significantly associated with Unified Parkinson's Disease Rating Scale and Parkinson's Disease Questionnaire-39. This study demonstrated that gait impairment occurs in the early stage of PD and deteriorates with the progression of the disease. The gait parameters mentioned above may help to detect PD earlier and assess the progression of PD.

Body-Worn Sensors for Parkinson’s disease: A qualitative approach with patients and healthcare professionals

Body-Worn Sensors (BWS) provide reliable objective and continuous assessment of Parkinson’s disease (PD) motor symptoms, but their implementation in clinical routine has not yet become widespread. Users’ perceptions of BWS have not been explored. This study intended to evaluate the usability, user experience (UX), patients’ perceptions of BWS, and health professionals’ (HP) opinions on BWS monitoring. A qualitative analysis was performed from semi-structured interviews conducted with 22 patients and 9 HP experts in PD. Patients completed two interviews before and after the BWS one-week experiment, and they answered two questionnaires assessing the usability and UX. Patients rated the three BWS usability with high scores (SUS median [range]: 87.5 [72.5–100]). The UX across all dimensions of their interaction with the BWS was positive. During interviews, all patients and HP expressed interest in BWS monitoring. Patients’ hopes and expectations increased the more they learned about BWS. They manifested enthusiasm to wear BWS, which they imagined could improve their PD symptoms. HP highlighted needs for logistical support in the implementation of BWS in their practice. Both patients and HP suggested possible uses of BWS monitoring in clinical practice, for treatment adjustments for example, or for research purposes. Patients and HP shared ideas about the use of BWS monitoring, although patients may be more likely to integrate BWS into their disease follow-up compared to HP in their practice. This study highlights gaps that need to be fulfilled to facilitate BWS adoption and promote their potential.

Intraoperative Quantification of MDS-UPDRS Tremor Measurements Using 3D Accelerometry: A Pilot Study

The most frequently used method for evaluating tremor in Parkinson’s disease (PD) is currently the internationally standardized Movement Disorder Society—Unified PD Rating Scale (MDS-UPDRS). However, the MDS-UPDRS is associated with limitations, such as its inherent subjectivity and reliance on experienced raters. Objective motor measurements using accelerometry may overcome the shortcomings of visually scored scales. Therefore, the current study focuses on translating the MDS-UPDRS tremor tests into an objective scoring method using 3D accelerometry. An algorithm to measure and classify tremor according to MDS-UPDRS criteria is proposed. For this study, 28 PD patients undergoing neurosurgical treatment and 26 healthy control subjects were included. Both groups underwent MDS-UPDRS tests to rate tremor severity, while accelerometric measurements were performed at the index fingers. All measurements were performed in an off-medication state. Quantitative measures were calculated from the 3D acceleration data, such as tremor amplitude and area-under-the-curve of power in the 4−6 Hz range. Agreement between MDS-UPDRS tremor scores and objective accelerometric scores was investigated. The trends were consistent with the logarithmic relationship between tremor amplitude and MDS-UPDRS score reported in previous studies. The accelerometric scores showed a substantial concordance (>69.6%) with the MDS-UPDRS ratings. However, accelerometric kinetic tremor measures poorly associated with the given MDS-UPDRS scores (R2 < 0.3), mainly due to the noise between 4 and 6 Hz found in the healthy controls. This study shows that MDS-UDPRS tremor tests can be translated to objective accelerometric measurements. However, discrepancies were found between accelerometric kinetic tremor measures and MDS-UDPRS ratings. This technology has the potential to reduce rater dependency of MDS-UPDRS measurements and allow more objective intraoperative monitoring of tremor.

[Assessment of mobility-Geriatric assessment instruments for mobility impairments and perspectives of instrumentation]

Mobility and its limitations play an important role in the quality of life of geriatric patients and influence activity and participation. The assessment of mobility is therefore of particular importance for treatment and treatment planning in geriatric patients. There is a variety of assessment tools that cannot be used in every patient group, e.g. due to floor effects. This article provides an overview of common assessment tools and facilitates the evaluation and use of these tools. Special consideration is given to performance-oriented aspects and current technical developments such as wearables.

Surrogates for rigidity and PIGD MDS-UPDRS subscores using wearable sensors

BACKGROUND

Telemedicine has the advantage of expanding access to care for patients with Parkinson's Disease (PD). However, rigidity and postural instability in PD are difficult to measure remotely, and are important measures of functional impairment and fall risk.

RESEARCH QUESTION

Can measures from wearable sensors be used as future surrogates for the MDS-UPDRS rigidity and Postural Instability and Gait Difficulty (PIGD) subscores?

METHODS

Thirty-one individuals with mild to moderate PD wore 3 inertial sensors at home for one week to measure quantity and quality of gait and turning in daily life. Separately, we performed a clinical assessment and balance characterization of postural sway with the same wearable sensors in the laboratory (On medication). We then first performed a traditional correlation analysis between clinical scores and objective measures of gait and balance followed by multivariable linear regression employing a best subset selection strategy.

RESULTS

The number of walking bouts and turns correlated significantly with the rigidity subscore, while the number of turns, foot pitch angle, and sway area while standing correlated significantly with the PIGD subscore (p < 0.05). The multivariable linear regression showed that rigidity subscore was best predicted by the number of walking bouts while the PIGD subscore was best predicted by a combination of number of walking bouts, gait speed, and postural sway.

SIGNIFICANCE

The correlation between objective sensor data and MDS-UPDRS rigidity and PIGD scores paves the way for future larger studies that evaluate use of objective sensor data to supplement remote MDS-UPDRS assessment.

Wearable Solutions for Patients with Parkinson's Disease and Neurocognitive Disorder: A Systematic Review

Prevalence of neurocognitive diseases in adult patients demands the use of wearable devices to transform the future of mental health. Recent development in wearable technology proclaimed its use in diagnosis, rehabilitation, assessment, and monitoring. This systematic review presents the state of the art of wearables used by Parkinson’s disease (PD) patients or the patients who are going through a neurocognitive disorder. This article is based on PRISMA guidelines, and the literature is searched between January 2009 to January 2020 analyzing four databases: PubMed, IEEE Xplorer, Elsevier, and ISI Web of Science. For further validity of articles, a new PEDro-inspired technique is implemented. In PEDro, five statistical indicators were set to classify relevant articles and later the citations were also considered to make strong assessment of relevant articles. This led to 46 articles that met inclusion criteria. Based on them, this systematic review examines different types of wearable devices, essential in improving early diagnose and monitoring, emphasizing their role in improving the quality of life, differentiating the various fitness and gait wearable-based exercises and their impact on the regression of disease and on the motor diagnosis tests and finally addressing the available wearable insoles and their role in rehabilitation. The research findings proved that sensor based wearable devices, and specially instrumented insoles, help not only in monitoring and diagnosis but also in tracking numerous exercises and their positive impact towards the improvement of quality of life among different Parkinson and neurocognitive patients.

Designing a mHealth clinical decision support system for Parkinson's disease: a theoretically grounded user needs approach

Background

Despite the established evidence and theoretical advances explaining human judgments under uncertainty, developments of mobile health (mHealth) Clinical Decision Support Systems (CDSS) have not explicitly applied the psychology of decision making to the study of user needs. We report on a user needs approach to develop a prototype of a mHealth CDSS for Parkinson’s disease (PD), which is theoretically grounded in the psychological literature about expert decision making and judgement under uncertainty.

Methods

A suite of user needs studies was conducted in 4 European countries (Greece, Italy, Slovenia, the UK) prior to the development of PD_Manager, a mHealth-based CDSS designed for Parkinson’s disease, using wireless technology. Study 1 undertook Hierarchical Task Analysis (HTA) including elicitation of user needs, cognitive demands and perceived risks/benefits (ethical considerations) associated with the proposed CDSS, through structured interviews of prescribing clinicians (N = 47). Study 2 carried out computational modelling of prescribing clinicians’ (N = 12) decision strategies based on social judgment theory. Study 3 was a vignette study of prescribing clinicians’ (N = 18) willingness to change treatment based on either self-reported symptoms data, devices-generated symptoms data or combinations of both.

Results

Study 1 indicated that system development should move away from the traditional silos of ‘motor’ and ‘non-motor’ symptom evaluations and suggest that presenting data on symptoms according to goal-based domains would be the most beneficial approach, the most important being patients’ overall Quality of Life (QoL). The computational modelling in Study 2 extrapolated different factor combinations when making judgements about different questions. Study 3 indicated that the clinicians were equally likely to change the care plan based on information about the change in the patient’s condition from the patient’s self-report and the wearable devices.

Conclusions

Based on our approach, we could formulate the following principles of mHealth design: 1) enabling shared decision making between the clinician, patient and the carer; 2) flexibility that accounts for diagnostic and treatment variation among clinicians; 3) monitoring of information integration from multiple sources. Our approach highlighted the central importance of the patient-clinician relationship in clinical decision making and the relevance of theoretical as opposed to algorithm (technology)-based modelling of human judgment.

Gait Classification Using Mahalanobis–Taguchi System for Health Monitoring Systems Following Anterior Cruciate Ligament Reconstruction

In this paper, a gait patterns classification system is proposed, which is based on Mahalanobis–Taguchi System (MTS). The classification of gait patterns is necessary in order to ascertain the rehab outcome among anterior cruciate ligament reconstruction (ACLR) patients. (1) Background: One of the most critical discussion about when ACLR patients should return to work (RTW). The objective was to use Mahalanobis distance (MD) to classify between the gait patterns of the control and ACLR groups, while the Taguchi Method (TM) was employed to choose the useful features. Moreover, MD was also utilised to ascertain whether the ACLR group approaching RTW. The combination of these two methods is called as Mahalanobis-Taguchi System (MTS). (2) Methods: This study compared the gait of 15 control subjects to a group of 10 subjects with laboratory. Later, the data were analysed using MTS. The analysis was based on 11 spatiotemporal parameters. (3) Results: The results showed that gait deviations can be identified successfully, while the ACLR can be classified with higher precision by MTS. The MDs of the healthy group ranged from 0.560 to 1.180, while the MDs of the ACLR group ranged from 2.308 to 1509.811. Out of the 11 spatiotemporal parameters analysed, only eight parameters were considered as useful features. (4) Conclusions: These results indicate that MTS can effectively detect the ACLR recovery progress with reduced number of useful features. MTS enabled doctors or physiotherapists to provide a clinical assessment of their patients with more objective way.

Monitoring Motor Symptoms During Activities of Daily Living in Individuals With Parkinson's Disease

This literature review addressed wearable sensor systems to monitor motor symptoms in individuals with Parkinson's disease (PD) during activities of daily living (ADLs). Specifically, progress in monitoring tremor, freezing of gait, dyskinesia, bradykinesia, and hypokinesia was reviewed. Twenty-seven studies were found that met the criteria of measuring symptoms in a home or home-like setting, with some studies examining multiple motor disorders. Accelerometers, gyroscopes, and electromyography sensors were included, with some studies using more than one type of sensor. Five studies measured tremor, five studies examined bradykinesia or hypokinesia, thirteen studies included devices to measure dyskinesia or motor fluctuations, and ten studies measured akinesia or freezing of gait. Current sensor technology can detect the presence and severity of each of these symptoms; however, most systems require sensors on multiple body parts, which is challenging for remote or ecologically valid observation. Different symptoms are detected by different sensor placement, suggesting that the goal of detecting all symptoms with a reduced set of sensors may not be achievable. For the goal of monitoring motor symptoms during ADLs in a home setting, the measurement system should be simple to use, unobtrusive to the wearer and easy for an individual with PD to put on and take off. Machine learning algorithms such as neural networks appear to be the most promising way to detect symptoms using a small number of sensors. More work should be done validating the systems during unscripted and unconstrained ADLs rather than in scripted motions.

Formal representation of ambulatory assessment protocols in HTML5 for human readability and computer execution

Ambulatory assessment (AA) is a research method that aims to collect longitudinal biopsychosocial data in groups of individuals. AA studies are commonly conducted via mobile devices such as smartphones. Researchers tend to communicate their AA protocols to the community in natural language by describing step-by-step procedures operating on a set of materials. However, natural language requires effort to transcribe onto and from the software systems used for data collection, and may be ambiguous, thereby making it harder to reproduce a study. Though AA protocols may also be written as code in a programming language, most programming languages are not easily read by most researchers. Thus, the quality of scientific discourse on AA stands to gain from protocol descriptions that are easy to read, yet remain formal and readily executable by computers. This paper makes the case for using the HyperText Markup Language (HTML) to achieve this. While HTML can suitably describe AA materials, it cannot describe AA procedures. To resolve this, and taking away lessons from previous efforts with protocol implementations in a system called TEMPEST, we offer a set of custom HTML5 elements that help treat HTML documents as executable programs that can both render AA materials, and effect AA procedures on computational platforms.

Capturing nighttime symptoms in Parkinson disease: Technical development and experimental verification of inertial sensors for nocturnal hypokinesia

Although nocturnal hypokinesia represents one of the most common nocturnal disabilities in Parkinson disease (PD), it is often a neglected problem in daily clinical practice. We have developed a portable ambulatory motion recorder (the NIGHT-Recorder), which consists of 16-bit triaxial integrated microelectromechanical system inertial sensors that are specifically designed to measure movements, register the position of the body with respect to gravity, and provide information on rotations on the longitudinal axis while lying in bed. The signal processing uses the forward derivative method to identify rolling over and getting out of bed as primary indicators. The prototype was tested on six PD pairs to measure their movements for one night. Using predetermined definitions, 134 movements were captured consisting of rolling over 115 times and getting out of bed 19 times. Patients with PD rolled over significantly fewer times than their spouses (p = 0.03), and the position change was significantly smaller in patients with PD (p = 0.03). Patients with PD rolled over at a significantly slower speed (p = 0.03) and acceleration (p = 0.03) than their spouses. In contrast, patients with PD got out of bed significantly more often than their spouses (p = 0.02). It is technically feasible to develop an easy-to-use, portable, and accurate device that can assist physicians in the assessment of nocturnal movements of patients with PD.

Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study

BACKGROUND

Application of objective measurement of stroke gait with accelerometer-based wearable technology and associated algorithms is increasing, despite reports questioning the accuracy of this technique in quantifying specific stroke-related gait impairments. The aim of this study is to determine the feasibility, validity and reliability of a low-cost open-source system incorporating algorithms and a single tri-axial accelerometer-based wearable to quantify gait characteristics in the laboratory and community post-stroke.

METHODS

Twenty-five participants with stroke wore the wearable (AX3, Axivity) on the lower back during a laboratory 2 minute continuous walk (preferred pace) on two occasions a week apart and continuously in the community for two consecutive 7 day periods. Video, instrumented walkway (GaitRite) and an OPAL accelerometer-based wearable were used as laboratory references.

RESULTS

Feasibility of the proposed system was good. The system was valid for measuring step count (ICC 0.899). Inherent differences in gait quantification between algorithm and GaitRite resulted in difficulties comparing agreement between the different systems. Agreement was moderate-excellent (ICC 0.503-0.936) for mean and variability gait characteristics vs. OPAL. Agreement was moderate-poor between the system and OPAL for asymmetry characteristics. Moderate-excellent reliability (ICC 0.534-0.857) was demonstrated for 11/14 laboratory measured gait characteristics. Community test-retest reliability was good-excellent (ICC 0.867-0.983) for all except one (ICC 0.699) of the 19 gait characteristics.

CONCLUSION

The proposed system is a low-cost, reliable tool for quantifying gait post-stroke with multiple potential applications. Further refinement to optimise gait quantification algorithms for certain gait characteristics including gait asymmetry is required.

Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study

Background

Application of objective measurement of stroke gait with accelerometer-based wearable technology and associated algorithms is increasing, despite reports questioning the accuracy of this technique in quantifying specific stroke-related gait impairments. The aim of this study is to determine the feasibility, validity and reliability of a low-cost open-source system incorporating algorithms and a single tri-axial accelerometer-based wearable to quantify gait characteristics in the laboratory and community post-stroke.

Methods

Twenty-five participants with stroke wore the wearable (AX3, Axivity) on the lower back during a laboratory 2 minute continuous walk (preferred pace) on two occasions a week apart and continuously in the community for two consecutive 7 day periods. Video, instrumented walkway (GaitRite) and an OPAL accelerometer-based wearable were used as laboratory references.

Results

Feasibility of the proposed system was good. The system was valid for measuring step count (ICC 0.899). Inherent differences in gait quantification between algorithm and GaitRite resulted in difficulties comparing agreement between the different systems. Agreement was moderate-excellent (ICC 0.503–0.936) for mean and variability gait characteristics vs. OPAL. Agreement was moderate-poor between the system and OPAL for asymmetry characteristics. Moderate-excellent reliability (ICC 0.534–0.857) was demonstrated for 11/14 laboratory measured gait characteristics. Community test-retest reliability was good-excellent (ICC 0.867–0.983) for all except one (ICC 0.699) of the 19 gait characteristics.

Conclusion

The proposed system is a low-cost, reliable tool for quantifying gait post-stroke with multiple potential applications. Further refinement to optimise gait quantification algorithms for certain gait characteristics including gait asymmetry is required.

Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease

Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The Parkinson@home study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort.

Validation of a Smartphone Application Measuring Motor Function in Parkinson's Disease

BACKGROUND

Measurement of motor function is critical to the assessment and management of Parkinson's disease. Ambulatory motor assessment has the potential to provide a glimpse of the patient's clinical state beyond the consultation. We custom-designed a smartphone application that quantitatively measures hand dexterity and hypothesized that this can give an indication of a patient's overall motor function.

OBJECTIVE

The aims of this study were to (i) validate this smartphone application against MDS-UPDRS motor assessment (MDS-UPDRS-III) and the two-target tapping test; (ii) generate a prediction model for MDS-UPDRS-III; (iii) assess repeatability of our smartphone application and (iv) examine compliance and user-satisfaction of this application.

METHODS

103 patients with Parkinson's disease were recruited from two movement disorders clinics. After initial assessment, a group of patients underwent repeat assessment within two weeks. Patients were invited to use the smartphone application at home over three days, followed by a survey to assess their experience.

RESULTS

Significant correlation between key smartphone application test parameters and MDS-UPDRS-III (r = 0.281-0.608, p < 0.0001) was demonstrated. A prediction model based on these parameters accounted for 52.3% of variation in MDS-UPDRS-III (R2 = 0.523, F(4,93) = 25.48, p < 0.0001). Forty-eight patients underwent repeat assessment under identical clinical conditions. Repeatability of key smartphone application tests parameters and predicted MDS-UPDRS-III was moderate to strong (intraclass correlation coefficient 0.584-0.763, p < 0.0001). The follow-up survey identified that our patients were very comfortable with the smartphone application and mobile technology.

CONCLUSIONS

Our smartphone application demonstrated satisfactory repeatability and validity when measured against MDS-UPDRS-III. Its performance is acceptable considering our smartphone application measures hand dexterity only.

Wearable sensors objectively measure gait parameters in Parkinson's disease

Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson's disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson's disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson's disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects' preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson's disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson's disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson's disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson's disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.

Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson's Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back

INTRODUCTION

Inertial measurement units (IMUs) positioned on various body locations allow detailed gait analysis even under unconstrained conditions. From a medical perspective, the assessment of vulnerable populations is of particular relevance, especially in the daily-life environment. Gait analysis algorithms need thorough validation, as many chronic diseases show specific and even unique gait patterns. The aim of this study was therefore to validate an acceleration-based step detection algorithm for patients with Parkinson's disease (PD) and older adults in both a lab-based and home-like environment.

METHODS

In this prospective observational study, data were captured from a single 6-degrees of freedom IMU (APDM) (3DOF accelerometer and 3DOF gyroscope) worn on the lower back. Detection of heel strike (HS) and toe off (TO) on a treadmill was validated against an optoelectronic system (Vicon) (11 PD patients and 12 older adults). A second independent validation study in the home-like environment was performed against video observation (20 PD patients and 12 older adults) and included step counting during turning and non-turning, defined with a previously published algorithm.

RESULTS

A continuous wavelet transform (cwt)-based algorithm was developed for step detection with very high agreement with the optoelectronic system. HS detection in PD patients/older adults, respectively, reached 99/99% accuracy. Similar results were obtained for TO (99/100%). In HS detection, Bland-Altman plots showed a mean difference of 0.002 s [95% confidence interval (CI) -0.09 to 0.10] between the algorithm and the optoelectronic system. The Bland-Altman plot for TO detection showed mean differences of 0.00 s (95% CI -0.12 to 0.12). In the home-like assessment, the algorithm for detection of occurrence of steps during turning reached 90% (PD patients)/90% (older adults) sensitivity, 83/88% specificity, and 88/89% accuracy. The detection of steps during non-turning phases reached 91/91% sensitivity, 90/90% specificity, and 91/91% accuracy.

CONCLUSION

This cwt-based algorithm for step detection measured at the lower back is in high agreement with the optoelectronic system in both PD patients and older adults. This approach and algorithm thus could provide a valuable tool for future research on home-based gait analysis in these vulnerable cohorts.

Gait Is Associated with Cognitive Flexibility: A Dual-Tasking Study in Healthy Older People

Objectives: To analyze which gait parameters are primarily influenced by cognitive flexibility, and whether such an effect depends on the walking condition used.

Design: Cross-sectional analysis.

Setting: Tübingen evaluation of Risk factors for Early detection of Neurodegenerative Disorders.

Participants: A total of 661 non-demented individuals (49–80 years).

Measurements: A gait assessment with four conditions was performed: a 20 m walk at convenient speed (C), at fast speed (F), at fast speed while checking boxes (FB), and while subtracting serial 7s (FS). Seven gait parameters from a wearable sensor-unit (McRoberts, Netherlands) were compared with delta Trail-Making-Test (dTMT) values, which is a measure of cognitive flexibility. Walking strategies of good and poor dTMT performers were compared by evaluating the patterns of gait parameters across conditions.

Results: Five parameters correlated significantly with the dTMT in the FS condition, two parameters in the F and FB condition, and none in the C condition. Overall correlations were relatively weak. Gait speed was the gait parameter that most strongly correlated with the dTMT (r² = 7.4%). In good, but not poor, dTMT performers differences between FB and FS were significantly different in variability-associated gait parameters.

Conclusion: Older individuals need cognitive flexibility to perform difficult walking conditions. This association is best seen in gait speed. New and particularly relevant for recognition and training of deficits is that older individuals with poor cognitive flexibility have obviously fewer resources to adapt to challenging walking conditions. Our findings partially explain gait deficits in older adults with poor cognitive flexibility.

Quantitative Analysis of Motor Status in Parkinson's Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications

Long-term and objective monitoring is necessary for full assessment of the condition of patients with Parkinson's disease (PD). Recent advances in biotechnology have seen the development of various types of wearable (body-worn) sensor systems. By using accelerometers and gyroscopes, these devices can quantify motor abnormalities, including decreased activity and gait disturbances, as well as nonmotor signs, such as sleep disturbances and autonomic dysfunctions in PD. This review discusses methodological problems inherent in wearable devices. Until now, analysis of the mean values of motion-induced signals on a particular day has been widely applied in the clinical management of PD patients. On the other hand, the reliability of these devices to detect various events, such as freezing of gait and dyskinesia, has been less than satisfactory. Quantification of disease-specific changes rather than nonspecific changes is necessary.

Algorithm for Turning Detection and Analysis Validated under Home-Like Conditions in Patients with Parkinson's Disease and Older Adults using a 6 Degree-of-Freedom Inertial Measurement Unit at the Lower Back

INTRODUCTION

Aging and age-associated disorders such as Parkinson's disease (PD) are often associated with turning difficulties, which can lead to falls and fractures. Valid assessment of turning and turning deficits specifically in non-standardized environments may foster specific treatment and prevention of consequences.

METHODS

Relative orientation, obtained from 3D-accelerometer and 3D-gyroscope data of a sensor worn at the lower back, was used to develop an algorithm for turning detection and qualitative analysis in PD patients and controls in non-standardized environments. The algorithm was validated with a total of 2,304 turns ≥90° extracted from an independent dataset of 20 PD patients during medication ON- and OFF-conditions and 13 older adults. Video observation by two independent clinical observers served as gold standard.

RESULTS

In PD patients under medication OFF, the algorithm detected turns with a sensitivity of 0.92, a specificity of 0.89, and an accuracy of 0.92. During medication ON, values were 0.92, 0.78, and 0.83. In older adults, the algorithm reached validation values of 0.94, 0.89, and 0.92. Turning magnitude (difference, 0.06°; SEM, 0.14°) and duration (difference, 0.004 s; SEM, 0.005 s) yielded high correlation values with gold standard. Overall accuracy for direction of turning was 0.995. Intra class correlation of the clinical observers was 0.92.

CONCLUSION

This wearable sensor- and relative orientation-based algorithm yields very high agreement with clinical observation for the detection and evaluation of ≥90° turns under non-standardized conditions in PD patients and older adults. It can be suggested for the assessment of turning in daily life.

Potential of APDM mobility lab for the monitoring of the progression of Parkinson's disease

APDM's Mobility Lab system provides portable, validated, reliable, objective measures of balance and gait that are sensitive to Parkinson's disease (PD). In this review, we describe the potential of objective measures collected with the Mobility Lab system for tracking longitudinal progression of PD. Balance and gait are among the most important motor impairments influencing quality of life for people with PD. Mobility Lab uses body-worn, Opal sensors on the legs, trunk and arms during prescribed tasks, such as the instrumented Get Up and Go test or quiet stance, to quickly quantify the quality of balance and gait in the clinical environment. The same Opal sensors can be sent home with patients to continuously monitor the quality of their daily activities. Objective measures have the potential to monitor progression of mobility impairments in PD throughout its course to improve patient care and accelerate clinical trials.

Test-Retest Reliability of Dual-Task Outcome Measures in People With Parkinson Disease

BACKGROUND

Dual-task (DT) training is gaining ground as a physical therapy intervention in people with Parkinson disease (PD). Future studies evaluating the effect of such interventions need reliable outcome measures. To date, the test-retest reliability of DT measures in patients with PD remains largely unknown.

OBJECTIVE

The purpose of this study was to assess the reliability of DT outcome measures in patients with PD.

DESIGN

A repeated-measures design was used.

METHODS

Patients with PD ("on" medication, Mini-Mental State Examination score ≥24) performed 2 cognitive tasks (ie, backward digit span task and auditory Stroop task) and 1 functional task (ie, mobile phone task) in combination with walking. Tasks were assessed at 2 time points (same hour) with an interval of 6 weeks. Test-retest reliability was assessed for gait while performing each secondary task (DT gait) for both cognitive tasks while walking (DT cognitive) and for the functional task while walking (DT functional).

RESULTS

Sixty-two patients with PD (age=39-89 years, Hoehn and Yahr stages II-III) were included in the study. Intraclass correlation coefficients (ICCs) showed excellent reliability for DT gait measures, ranging between .86 and .95 when combined with the digit span task, between .86 and .95 when combined with the auditory Stroop task, and between .72 and .90 when combined with the mobile phone task. The standard error of measurements for DT gait speed varied between 0.06 and 0.08 m/s, leading to minimal detectable changes between 0.16 and 0.22 m/s. With regard to DT cognitive measures, reaction times showed good-to-excellent reliability (digit span task: ICC=.75; auditory Stroop task: ICC=.82).

LIMITATIONS

The results cannot be generalized to patients with advanced disease or to other DT measures.

CONCLUSIONS

In people with PD, DT measures proved to be reliable for use in clinical studies and look promising for use in clinical practice to assess improvements after DT training. Large effects, however, are needed to obtain meaningful effect sizes.

New methods for the assessment of Parkinson's disease (2005 to 2015): A systematic review

BACKGROUND

The past decade has witnessed a highly dynamic and growing expansion of novel methods aimed at improving the assessment of Parkinson's disease with technology (NAM-PD) in laboratory, clinical, and home environments. However, the current state of NAM-PD regarding their maturity, feasibility, and usefulness in assessing the main PD features has not been systematically evaluated.

METHODS

A systematic review of articles published in the field from 2005 to 2015 was performed. Of 9,503 publications identified in PubMed and the Web of Science, 848 full papers were evaluated, and 588 original articles were assessed to evaluate the technological, demographic, clinimetric, and technology transfer readiness parameters of NAM-PD.

RESULTS

Of the studies, 65% included fewer than 30 patients, < 50% employed a standard methodology to validate diagnostic tests, 8% confirmed their results in a different dataset, and 87% occurred in a clinic or lab. The axial features domain was the most frequently studied, followed by bradykinesia. Rigidity and nonmotor domains were rarely investigated. Only 6% of the systems reached a technology level that justified the hope of being included in clinical assessments in a useful time period.

CONCLUSIONS

This systematic evaluation provides an overview of the current options for quantitative assessment of PD and what can be expected in the near future. There is a particular need for standardized and collaborative studies to confirm the results of preliminary initiatives, assess domains that are currently underinvestigated, and better validate the existing and upcoming NAM-PD. © 2016 International Parkinson and Movement Disorder Society.

Gait variability in Parkinson's disease: levodopa and walking direction

BACKGROUND

Levodopa treatment has been shown to improve gait spatio-temporal characteristics in both forward and backward walking. However, effect of levodopa on gait variability during backward walking compared with forward walking has not been reported.

AIMS OF STUDY

To study the effects of levodopa on gait variability of forward and backward walking in individuals with Parkinson's disease (PD).

METHODS

Forty individuals with PD were studied. Their mean age was 68.70 ± 7.46 year. The average time since diagnosis was 9.41 ± 5.72 year. Gait variability was studied while 'OFF' and 'ON' levodopa when the participants walked forward and backward at their usual speed. Variability in step time, swing time, stride length, double support time, and stride velocity were compared between medication condition and walking direction.

RESULTS

Variability of step time, swing time, stride length, and stride velocity decreased significantly during forward and backward walks (P < 0.001; P < 0.001; P = 0.003, P = 0.001, respectively) after levodopa administration. Variability of double support time was not changed after levodopa administration (P = 0.054).

CONCLUSIONS

Levodopa had positive effects on gait variability of forward and backward walking in individuals with PD. However, variability in double support time was not affected by the levodopa.

Wearable inertial sensors for human movement analysis

INTRODUCTION

The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.

AREAS COVERED

Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice. Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine.

Detection of Levodopa Induced Dyskinesia in Parkinson's Disease patients based on activity classification

In this paper, we present an activity classification-based algorithm for the automatic detection of Levodopa Induced Dyskinesia in Parkinson's Disease (PD) patients. Two PD patients experiencing motor fluctuations related to chronic Levodopa therapy performed a protocol of simple daily life activities on at least two different occasions. A Random Forest classifier was able to classify the performed activities by the patients with an overall accuracy of 86%. Based on the detected activity, a K Nearest Neighbor classifier detected the presence of dyskinesia with accuracy ranging from 75% to 88%.

Association between Community Ambulation Walking Patterns and Cognitive Function in Patients with Parkinson's Disease: Further Insights into Motor-Cognitive Links

Background. Cognitive function is generally evaluated based on testing in the clinic, but this may not always reflect real-life function. We tested whether parameters derived from long-term, continuous monitoring of gait are associated with cognitive function in patients with Parkinson's disease (PD). Methods. 107 patients with PD (age: 64.9 ± 9.3 yrs; UPDRS motor sum “off”: 40.4 ± 13.2; 25.23% women) wore a 3D accelerometer on their lower back for 3 days. Computerized measures of global cognitive function, executive function, attention, and nonverbal memory were assessed. Three-day acceleration derived measures included cadence, variability, bilateral coordination, and dynamic postural control. Associations between the acceleration derived measures and cognitive function were determined. Results. Linear regression showed associations between vertical gait variability and cadence and between global cognitive score, attention, and executive function (p ≤ 0.048). Dynamic postural control was associated with global cognitive score and attention (p ≤ 0.027). Nonverbal memory was not associated with the acceleration-derived measures. Conclusions. These findings suggest that metrics derived from a 3-day worn body-fixed sensor reflect cognitive function, further supporting the idea that the gait pattern may be altered as cognition declines and that gait provides a window into cognitive function in patients with PD.

The Parkinsonian Gait Spatiotemporal Parameters Quantified by a Single Inertial Sensor before and after Automated Mechanical Peripheral Stimulation Treatment

This study aims to evaluate the change in gait spatiotemporal parameters in subjects with Parkinson's disease (PD) before and after Automated Mechanical Peripheral Stimulation (AMPS) treatment. Thirty-five subjects with PD and 35 healthy age-matched subjects took part in this study. A dedicated medical device (Gondola) was used to administer the AMPS. All patients with PD were treated in off levodopa phase and their gait performances were evaluated by an inertial measurement system before and after the intervention. The one-way ANOVA for repeated measures was performed to assess the differences between pre- and post-AMPS and the one-way ANOVA to assess the differences between PD patients and the control group. Spearman's correlations assessed the associations between patients with PD clinical status (H&Y) and the percentage of improvement of the gait variables after AMPS (α < 0.05 for all tests). The PD group had an improvement of 14.85% in the stride length; 14.77% in the gait velocity; and 29.91% in the gait propulsion. The correlation results showed that the higher the H&Y classification, the higher the stride length percentage of improvement. The treatment based on AMPS intervention seems to induce a better performance in the gait pattern of PD patients, mainly in intermediate and advanced stages of the condition.

A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal Gait Parameters

Gait impairments are among the most disabling symptoms in several musculoskeletal and neurological conditions, severely limiting personal autonomy. Wearable gait sensors have been attracting attention as diagnostic tool for gait and are emerging as promising tool for tutoring and guiding gait execution. If their popularity is continuously growing, still there is room for improvement, especially towards more accurate solutions for spatio-temporal gait parameters estimation. We present an implementation of a zero-velocity-update gait analysis system based on a Kalman filter and off-the-shelf shoe-worn inertial sensors. The algorithms for gait events and step length estimation were specifically designed to comply with pathological gait patterns. More so, an Android app was deployed to support fully wearable and stand-alone real-time gait analysis. Twelve healthy subjects were enrolled to preliminarily tune the algorithms; afterwards sixteen persons with Parkinson's disease were enrolled for a validation study. Over the 1314 strides collected on patients at three different speeds, the total root mean square difference on step length estimation between this system and a gold standard was 2.9%. This shows that the proposed method allows for an accurate gait analysis and paves the way to a new generation of mobile devices usable anywhere for monitoring and intervention.

MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy

Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users’ health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson’s disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

Role of body-worn movement monitor technology for balance and gait rehabilitation

This perspective article will discuss the potential role of body-worn movement monitors for balance and gait assessment and treatment in rehabilitation. Recent advances in inexpensive, wireless sensor technology and smart devices are resulting in an explosion of miniature, portable sensors that can quickly and accurately quantify body motion. Practical and useful movement monitoring systems are now becoming available. It is critical that therapists understand the potential advantages and limitations of such emerging technology. One important advantage of obtaining objective measures of balance and gait from body-worn sensors is impairment-level metrics characterizing how and why functional performance of balance and gait activities are impaired. Therapy can then be focused on the specific physiological reasons for difficulty in walking or balancing during specific tasks. A second advantage of using technology to measure balance and gait behavior is the increased sensitivity of the balance and gait measures to document mild disability and change with rehabilitation. A third advantage of measuring movement, such as postural sway and gait characteristics, with body-worn sensors is the opportunity for immediate biofeedback provided to patients that can focus attention and enhance performance. In the future, body-worn sensors may allow therapists to perform telerehabilitation to monitor compliance with home exercise programs and the quality of their natural mobility in the community. Therapists need technological systems that are quick to use and provide actionable information and useful reports for their patients and referring physicians. Therapists should look for systems that provide measures that have been validated with respect to gold standard accuracy and to clinically relevant outcomes such as fall risk and severity of disability.