@conference{timmermans_generalizable_2025,

title = {A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s disease},

author = {N. A. Timmermans and R. Terranova and D. C. Soriano and H. Cagnan and Y. P. Raykov and I. G. Bucur and B. R. Bloem and R. C. Helmich and L. J. W. Evers},

url = {https://www.nature.com/articles/s41531-025-01056-2},

doi = {10.1038/s41531-025-01056-2},

issn = {2373-8057},

year  = {2025},

date = {2025-07-01},

urldate = {2025-07-01},

journal = {npj Parkinson's Disease},

volume = {11},

number = {1},

pages = {205},

abstract = {Wearable sensors can objectively and continuously monitor daily-life tremor in Parkinson’s Disease (PD). We developed an open-source algorithm for real-life monitoring of PD tremor which achieves generalizable performance across different wrist-worn devices. We achieved this using a unique combination of two independent, complementary datasets. The first was a small, but extensively video-labeled gyroscope dataset collected during unscripted activities at home (n = 24 PD; n = 24 controls). We used this to train and validate a logistic regression tremor detector based on cepstral coefficients. The second was a large, unsupervised dataset (n = 517 PD; n = 50 controls, data collected for 2 weeks with a different device), used to externally validate the algorithm. Results show that our algorithm can reliably quantify real-life PD tremor (sensitivity of 0.61 (0.20) and specificity of 0.97 (0.05)). Weekly aggregated tremor time and power showed excellent test-retest reliability and moderate correlation to MDS-UPDRS rest tremor scores. This opens possibilities to support clinical trials and individual tremor management with wearable technology.},

note = {Publisher: Nature Publishing Group},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{post_quantifying_2025,

title = {Quantifying arm swing in Parkinson's disease: a method accounting for arm activities during free-living gait},

author = {E. Post and T. Laarhoven and Y. P. Raykov and M. A. Little and J. Nonnekes and T. M. Heskes and B. R. Bloem and L. J. W. Evers},

doi = {10.1186/s12984-025-01578-z},

issn = {1743-0003},

year  = {2025},

date = {2025-02-01},

urldate = {2025-02-01},

journal = {Journal of Neuroengineering and Rehabilitation},

volume = {22},

number = {1},

pages = {37},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}