Ambulatory Parkinson's Disease Research Articles

Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations.

This research aims to extract human gait parameters from floor vibrations. The proposed approach provides an innovative methodology on occupant activity, contributing to a broader understanding of how human movements interact within their built environment. A multilevel probabilistic model was developed to estimate cadence and walking speed through the analysis of floor vibrations induced by walking. The model addresses challenges related to missing or incomplete information in the floor acceleration signals. Following the Bayesian Analysis Reporting Guidelines (BARG) for reproducibility, the model was evaluated through twenty-seven walking experiments, capturing floor vibration and data from Ambulatory Parkinson's Disease Monitoring (APDM) wearable sensors. The model was tested in a real-time implementation where ten individuals were recorded walking at their own selected pace. Using a rigorous combined decision criteria of 95% high posterior density (HPD) and the Range of Practical Equivalence (ROPE) following BARG, the results demonstrate satisfactory alignment between estimations and target values for practical purposes. Notably, with over 90% of the 95% HPD falling within the region of practical equivalence, there is a solid basis for accepting the estimations as probabilistically aligned with the estimations using the APDM sensors and video recordings. This research validates the probabilistic multilevel model in estimating cadence and walking speed by analyzing floor vibrations, demonstrating its satisfactory comparability with established technologies such as APDM sensors and video recordings. The close alignment between the estimations and target values emphasizes the approach's efficacy. The proposed model effectively tackles prevalent challenges associated with missing or incomplete data in real-world scenarios, enhancing the accuracy of gait parameter estimations derived from floor vibrations. Extracting gait parameters from floor vibrations could provide a non-intrusive and continuous means of monitoring an individual's gait, offering valuable insights into mobility and potential indicators of neurological conditions. The implications of this research extend to the development of advanced gait analysis tools, offering new perspectives on assessing and understanding walking patterns for improved diagnostics and personalized healthcare.Clinical and Translational Impact Statement: This manuscript introduces an innovative approach for unattended gait assessments with potentially significant implications for clinical decision-making. By utilizing floor vibrations to estimate cadence and walking speed, the technology can provide clinicians with valuable insights into their patients' mobility and functional abilities in real-life settings. The strategic installation of accelerometers beneath the flooring of homes or care facilities allows for uninterrupted daily activities during these assessments, reducing the reliance on specialized clinical environments. This technology enables continuous monitoring of gait patterns over time and has the potential for integration into healthcare platforms. Such integration can enhance remote monitoring, leading to timely interventions and personalized care plans, ultimately improving clinical outcomes. The probabilistic nature of our model enables uncertainty quantification in the estimated parameters, providing clinicians with a nuanced understanding of data reliability.

F62. Automatic detection of ON/OFF states in Parkinson disease patients using wearable inertial sensors

Introduction Reliably detecting ON/OFF states is important for monitoring PD treatment and progression. Currently, subjective patient diaries capture this information. We investigate if detection of motor signs of ON/OFF states can be achieved by using kinematic measurements from wearable sensor technology combined with a machine learning (ML) pipeline. Methods Twenty-five PD subjects (19 males, 69 ± 7 years) taking levodopa performed 10-m Instrumented Stand and Walk (ISAW) tests in their ON and OFF states while wearing Ambulatory Parkinson Disease Monitoring (APDM) sensors on their sternum, wrists, lumbar and lower extremities. A neurologist scored each ISAW according to the MDS-UPDRS-III. We analyzed 98 kinematic features for significance to neurologist total motor score and ON/OFF using both statistical (repeated-measures ANOVA, step-wise mixed-model regression, likelihood-ratio test, ridge regression) and ML methods. Results Twenty-two features significantly differed between patient reported ON/OFF states, with the most significant being trunk transverse range-of-motion (RofM), arm RofM, mid-swing elevation, stride length, turn velocity, steps in turn and toe out angles. Estimates from regression model showed average difference of 14 points between OFF/ON states in total UPDRS score and 9 points when adjusted for 5 significant features for individual baseline (mean trunk transverse RofM, right arm RofM, and toe out angle having highest effect; coeff. −8.67, −5.25, −3.36 respectively). Several approaches were employed for predicting ON/OFF states based on these features: direct binary classification (acc = 0.56), regression to total UPDRS score (acc = 0.76), regression to PIGD sub-score (acc = 0.64), and classification of ON–OFF/OFF–ON transitions using feature differences (Naive Bayes: acc = 0.74, AUC = 0.78; Random Forest: acc = 0.76, AUC = 0.90). Conclusion Wearable inertial sensors hold promise for detecting ON/OFF states in PD patients using an augmented ML approach. This could be particularly useful for monitoring response to therapy in an outpatient setting.

Pain in Parkinson disease: a local single-center correlational study of ambulatory Parkinson disease patients

Pain in Parkinson disease: a local single-center correlational study of ambulatory Parkinson disease patients

A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson's disease.

BackgroundThere is growing interest in having objective assessment of health-related outcomes using technology-based devices that provide unbiased measurements which can be used in clinical practice and scientific research. Many studies have investigated the clinical manifestations of Parkinson’s disease using such devices. However, clinimetric properties and clinical validation vary among the different devices.MethodsGiven such heterogeneity, we sought to perform a systematic review in order to (i) list, (ii) compare and (iii) classify technological-based devices used to measure motor function in individuals with Parkinson's disease into three groups, namely wearable, non-wearable and hybrid devices. A systematic literature search of the PubMed database resulted in the inclusion of 168 studies. These studies were grouped based on the type of device used. For each device we reviewed availability, use, reliability, validity, and sensitivity to change. The devices were then classified as (i) ‘recommended’, (ii) ‘suggested’ or (iii) ‘listed’ based on the following criteria: (1) used in the assessment of Parkinson’s disease (yes/no), (2) used in published studies by people other than the developers (yes/no), and (3) successful clinimetric testing (yes/no).ResultsSeventy-three devices were identified, 22 were wearable, 38 were non-wearable, and 13 were hybrid devices. In accordance with our classification method, 9 devices were ‘recommended’, 34 devices were ‘suggested’, and 30 devices were classified as ‘listed’. Within the wearable devices group, the Mobility Lab sensors from Ambulatory Parkinson’s Disease Monitoring (APDM), Physilog®, StepWatch 3, TriTrac RT3 Triaxial accelerometer, McRoberts DynaPort, and Axivity (AX3) were classified as ‘recommended’. Within the non-wearable devices group, the Nintendo Wii Balance Board and GAITRite® gait analysis system were classified as ‘recommended’. Within the hybrid devices group only the Kinesia® system was classified as ‘recommended’.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-016-0136-7) contains supplementary material, which is available to authorized users.

Punding prevalence in Parkinson's disease

The purpose of this study was to determine punding prevalence in an ambulatory Parkinson's disease (PD) population. We conducted a patient-and-caregiver-completed punding survey in 373 consecutive patients in an academic ambulatory center. Completion rate was 78%. Only four patients were identified as punding. Patients did not retain insight to their behavior. Forty patients with high-dose levodopa monotherapy or levodopa and dopamine agonist treatment had physician-administered interview. None had punding. Punding incidence was low in this patient group (1.4%) in contrast with previous reports of 14%. Despite the low incidence, this behavior is disruptive and should be carefully elicited by physicians caring for Parkinson's disease patients.

Home visiting by nursing students to patients with Parkinson's disease.

Thirty ambulatory Parkinson's disease patients diagnosed with Stage I, II, and III illness were visited at home by 15 senior baccalaureate nursing students on a weekly basis for one year. The purpose of the home visit was to assess the patient, implement a regimen of mild exercise, and increase the patient's social network. Twenty-nine patients completed one year of weekly exercise sessions under the supervision of a nursing student. This article includes a demographic description of the sample, an outline of the exercise regimen, a report on the assessment tool that was used to collect the data, and a discussion of the program's educational value for the students. Peripheral activities such as organization of the support group and education of caregivers are discussed. Requirements of the study are explained. Analysis of the collected data is under way. An empirical report is forthcoming this year.