Arm swing deviations in patients with Parkinson's disease at different gait velocities

Parkinson's disease participant-led research on the impact of deep brain stimulation frequency

Parkinson's disease symptoms, including gait disturbances and speech impairment, substantially impact quality of life. This n-of-1 study examined the effects of deep brain stimulation (DBS) frequency on a 54-year-old man with Parkinson's disease. The participant conceived and led the study, embedding patient insight within the project design and execution. The impacts of three DBS frequencies were assessed using markerless motion capture for gait, voice recording for speech analysis, and Kinarm robotic tasks of upper limb coordination and cognitive assessment. The study found that DBS frequency has a clinically significant impact on multiple symptoms, including gait, rigidity, speech and cognitive-motor ability.

Quantifying arm swing in Parkinson's disease: a method accounting for arm activities during free-living gait

BACKGROUND

Accurately measuring hypokinetic arm swing during free-living gait in Parkinson's disease (PD) is challenging due to other concurrent arm activities. We developed a method to isolate gait segments without these arm activities.

METHODS

Wrist accelerometer and gyroscope data were collected from 25 individuals with PD and 25 age-matched controls while performing unscripted activities in their home environment. This was done after overnight withdrawal of dopaminergic medication ('pre-medication') and approximately one hour after intake ('post-medication'). Using video annotations as ground truth, we trained and evaluated two classifiers: one for detecting gait and one for detecting gait segments without other arm activities. Based on the filtered gait segments, arm swing was quantified using the median and 95th percentile range of motion (RoM). These arm swing parameters were evaluated in three ways: (1) the agreement between predicted and video-annotated gait segments without other arm activities, (2) the sensitivity to differences between PD and controls, and (3) the sensitivity to the effects of dopaminergic medication.

RESULTS

On the most affected side, the mean (SD) balanced accuracy for detecting gait without other arm activities was 0.84 (0.10) pre-medication and 0.88 (0.09) post-medication. The agreement between arm swing parameters of predicted and video-annotated gait segments without other arm activities was high irrespective of medication state (intra-class correlation coefficients: median RoM: 0.99; 95th percentile RoM: 0.97). Both the median and 95th percentile RoM were smaller in PD pre-medication compared to controls (median: Δ = - 18 . 80 ∘ , 95% CI [ - 30.63, - 10.60], p < 0.001; 95th percentile: Δ = - 28 . 34 ∘ , 95% CI [ - 38.26, - 18.18], p < 0.001), and smaller in pre- compared to post-medication (median: Δ = - 12 . 31 ∘ , 95% CI [ - 21.35, - 5.59], p < 0.001; 95th percentile: Δ = - 19 . 04 ∘ , 95% CI [ - 28.48, - 11.14], p < 0.001). The differences in RoM between pre- and post-medication were larger after filtering gait for the median (p < 0.01) and 95th percentile RoM (p = 0.01).

CONCLUSIONS

Filtering out gait segments with other concurrent arm activities is feasible and increases the change in arm swing parameters following dopaminergic medication in free-living conditions. This approach may be used to monitor treatment effect and disease progression in daily life.

Identifying Subtle Motor Deficits Before Parkinson's Disease is Diagnosed: What to Look for?

Motor deficits typical of Parkinson's disease (PD), such as gait and balance disturbances, tremor, reduced arm swing and finger movement, and voice and breathing changes, are believed to manifest several years prior to clinical diagnosis. Here we describe the evidence for the presence and progression of motor deficits in this pre-diagnostic phase in order to provide suggestions for the design of future observational studies for an effective, quantitatively oriented investigation. On the one hand, these future studies must detect these motor deficits in as large (potentially, population-based) cohorts as possible with high sensitivity and specificity. On the other hand, they must describe the progression of these motor deficits in the pre-diagnostic phase as accurately as possible, to support the testing of the effect of pharmacological and non-pharmacological interventions. Digital technologies and artificial intelligence can substantially accelerate this process.

Detection and Characterization of Walking Bouts Using a Single Wrist-Worn Accelerometer in Free-living Conditions

Detection and characterization of abnormalities of movement are important to develop a method for detecting early signs of Parkinson's disease (PD). Most of the current research in detection of characteristic reduction of movements due to PD, known as parkinsonism, requires using a set of invasive sensors in a clinical or controlled environment. Actigraphy has been widely used in medical research as a non-invasive data acquisition method in free-living conditions for long periods of time. The proposed algorithm uses triaxial accelerometer data obtained through actigraphy to detect walking bouts at least 10 seconds long and characterize them using cadence and arm swing. Accurate detection of walking periods is the first step toward the characterization of movement based on gait abnormalities. The algorithm was based on a Walking Score (WS) derived using the value of the auto-correlation function (ACF) for the Resultant acceleration vector. The algorithm achieved a precision of 0.90, recall of 0.77, and F1 score of 0.83 compared to the expert scoring for walking bout detection. We additionally described a method to measure arm swing amplitude.