BackgroundWearable sensors have the potential to provide clinicians with access to motor performance of people with movement disorder as they undergo intervention. However, sensor data often have to be manually classified and segmented before they can be processed into clinical metrics. This process can be time consuming. We recently proposed detection and segmentation algorithms based on peak detection using Inertial Measurement Units (IMUs) to automatically identify and isolate common activities during daily living such as standing up, walking, turning, and sitting down. These algorithms were developed using a homogenous population of healthy older adults. The aim of this study was to investigate the transferability of these algorithms in people with Parkinson’s disease (PD).MethodsA modified Timed Up And Go task was used since it is comprised of these activities, all performed in a continuous fashion. Twelve older adults diagnosed with early PD (Hoehn & Yahr ≤ 2) were recruited for the study and performed three trials of a 10 and 5-m TUG during OFF state. They were outfitted with 17 IMUs covering each body segment. Raw data from IMUs were detrended, normalized and filtered to reveal kinematics peaks that corresponded to different activities. Segmentation was accomplished by identifying the first minimum or maximum to the right and the left of these peaks. Segmentation times were compared to results from two examiners who visually segmented the activities. Specificity and sensitivity were used to evaluate the accuracy of the detection algorithms.ResultsUsing the same IMUs and algorithms developed in the previous study, we were able to detect these activities with 97.6% sensitivity and 92.7% specificity (n = 432) in PD population. However, with modifications to the IMUs selection, we were able to detect these activities with 100% accuracy. Similarly, applying the same segmentation to PD population, we were able to isolate these activities within ~500 ms of the visual segmentation. Re-optimizing the filtering frequencies, we were able to reduce this difference to ~400 ms.ConclusionsThis study demonstrates the agility and transferability of using a system of IMUs to accurately detect and segment activities in daily living in people with movement disorders.
Wearable sensors such as inertial measurement units (IMUs) have been widely used to measure the quantity of physical activities during daily living in healthy and people with movement disorders through activity classification. These sensors have the potential to provide valuable information to evaluate the quality of the movement during the activities of daily living (ADL), such as walking, sitting down, and standing up, which could help clinicians to monitor rehabilitation and pharmaceutical interventions. However, high accuracy in the detection and segmentation of these activities is necessary for proper evaluation of the quality of the performance within a given segment. This paper presents algorithms to process IMU data, to detect and segment unstructured ADL in people with Parkinson's disease (PD) in simulated free-living environment. The proposed method enabled the detection of 1610 events of ADL performed by nine community dwelling older adults with PD under simulated free-living environment with 90% accuracy (sensitivity = 90.8%, specificity = 97.8%) while segmenting these activities within 350 ms of the "gold-standard" manual segmentation. These results demonstrate the robustness of the proposed method to eventually be used to automatically detect and segment ADL in free-living environment in people with PD. This could potentially lead to a more expeditious evaluation of the quality of the movement and administration of proper corrective care for patients who are under physical rehabilitation and pharmaceutical intervention for movement disorders.
Background:Clinical and anecdotal observations propose that patients with Parkinson’s disease (PD) may show drug-induced dyskinesia (DID) concomitantly with cardinal motor features. However, the extent of the concomitant presence of DID and cardinal features remains to be determined.Objectives:This cross-sectional study measured peak-dose choreic-type DID in a quantitative manner in patients diagnosed with PD, and determined whether symptoms such as tremor, bradykinesia, rigidity, postural instability or freezing of gait (FoG) were still detectable in these patients.Methods:89 patients diagnosed with PD were recruited and assessed using a combination of quantitative measures using inertial measurement units to capture DID, tremor, bradykinesia, and FoG. Clinical evaluations were also used to assess rigidity and postural instability. Motor symptoms of PD were assessed 3 times during the testing period, and a series of activities of daily living were repeated twice, in between clinical tests, during which the level of DID was quantified. Peak-dose was identified as the period during which patients had the highest levels of DID. Levels of tremor, rigidity, bradykinesia, postural instability, and FoG were used to determine the percentage of patients showing these motor symptoms simultaneously with DID.Results:72.4% of patients tested presented with measurable DID during the experiment. Rest, postural and kinetic tremor (12.7% , 38.1% , and 15.9% respectively), bradykinesia (28.6% ), rigidity (55.6% ), postural instability (71.4% ) and FoG (9.5% ) were detected simultaneously with DID.Conclusions:PD symptomatology remains present in patients showing peak-dose choreic-type DID, illustrating the challenge facing physicians when trying to avoid dyskinesia while attempting to alleviate motor symptoms.
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