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The study examined how clinically measured walking capacity contributes to real-world walking performance in persons with Parkinson's disease (PD).Cross-sectional baseline data (n = 82) from a PD clinical trial were analyzed. The 6-Minute Walk Test (6MWT) and 10-Meter Walk Test (10MWT) were used to generate capacity metrics of walking endurance and fast gait speed, respectively. An activity monitor worn for seven days was used to generate performance metrics of mean daily steps and weekly moderate intensity walking minutes. Univariate linear regression analyses were used to examine associations between each capacity and performance measure in the full sample and less and more active subgroups.Walking capacity significantly contributed to daily steps in the full sample (endurance: RWalking capacity contributed to, but explained a relatively small portion of the variance in, real-world walking performance. The contribution was somewhat greater in less active individuals. The study adds support to the idea that clinically measured walking capacity may have limited benefit for understanding real-world walking performance in PD. Factors beyond walking capacity may better account for actual walking behavior.

Parkinson’s disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people’s bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients’ daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

BACKGROUND AND PURPOSE Walking activity in persons with Parkinson disease (PD) is important for preventing functional decline. The contribution of walking activity to home and community mobility in PD is poorly understood. METHODS Cross-sectional baseline data (N = 69) were analyzed from a randomized controlled PD trial. The Life-Space Assessment (LSA) quantified the extent, frequency, and independence across 5 expanding levels of home and community mobility, producing individual subscores and a total score. Two additional summed scores were used to represent mobility within (Levels 1-3) and beyond (Levels 4-5) neighborhood limits. An accelerometer measured walking activity for 7 days. Regression and correlation analyses evaluated relationships between daily steps and mobility scores. Mann-Whitney U tests secondarily compared differences in mobility scores between the active and sedentary groups. RESULTS Walking activity contributed significantly to the summed Level 1-3 score (β = 0.001, P = 0.004) but not to the summed Level 4-5 (β = 0.001, P = 0.33) or total (β = 0.002, P = 0.07) scores. Walking activity was significantly related to Level 1 (ρ = 0.336, P = 0.005), Level 2 (ρ = 0.307, P = 0.010), and Level 3 (ρ = 0.314, P = 0.009) subscores. Only the summed Level 1-3 score (P = 0.030) was significantly different between the active and sedentary groups. DISCUSSION AND CONCLUSIONS Persons with PD who demonstrated greater mobility beyond the neighborhood were not necessarily more active; walking activity contributed more so to home and neighborhood mobility. Compared with LSA total score, the Level 1-3 summed score may be a more useful participation-level measure for assessing the impact of changes in walking activity.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1 available at: http://links.lww.com/JNPT/A349).

In this paper, we present ExerciseCheck. ExerciseCheck is an interactive computer vision system that is sufficiently modular to work with different sources of human pose estimates, i.e., estimates from deep or traditional models that interpret RGB or RGB-D camera input. In a pilot study, we first compare the pose estimates produced by four deep models based on RGB input with those of the MS Kinect based on RGB-D data. The results indicate a performance gap that required us to choose the MS Kinect when we tested ExerciseCheck with Parkinson's disease patients in their homes. ExerciseCheck is capable of customizing exercises, capturing exercise information, evaluating patient performance, providing therapeutic feedback to the patient and the therapist, checking the progress of the user over the course of the physical therapy, and supporting the patient throughout this period. We conclude that ExerciseCheck is a user-friendly computer vision application that can assist patients by providing motivation and guidance to en-sure correct execution of the required exercises. Our re-sults also suggest that while there has been considerable progress in the field of pose estimation using deep learning, current deep learning models are not fully ready to replace RGB-D sensors, especially when the exercises involved are complex, and the patient population being accounted for has to be carefully tracked for its "active range of motion."

Home-based exercising is a vital part of any physical therapy program. With correct execution of the exercises, faster recovery from physical impairments can be achieved. With the conventional approaches, however, a home-based physical therapy program may not be as effective due to the lack of supervision by the therapist at home. ExerciseCheck is designed as a remote monitoring and evaluation platform for individuals involved in home-based physical therapy. The goal of ExerciseCheck is to provide patients and physical therapists with real-time visual feedback and quantitative analysis. In this paper, we discuss the progress we have made toward (1) a more comprehensive analysis of the performance of the patient and (2) informative feedback to the patient and the physical therapist. Furthermore, in order to validate the feasibility and effectiveness of our system, we have performed an experiment in a clinical setting involving five patients with Parkinson disease. We describe how ExerciseCheck can quantitatively evaluate the user's performance and identify their problems. We assessed how ExerciseCheck can impact the performance and the overall experience of the patient. Our results show that ExerciseCheck is a user-friendly system that patients and physical therapists can easily interact with. Patients benefit from visual and quantitative feedback and apply this to subsequent repetitions of the exercises.