Marcin Strączkiewicz
@MStraczkiewicz
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Research Associate at Harvard TH Chan School of Public Health. @jponnela Lab member. Wearables, digital phenotyping, and physical activity.
Boston, MA
Joined October 2020
I’m happy to share our latest publication in the Journal of NeuroEngineering and Rehabilitation titled “Free-living monitoring of ALS progression in upper limbs using wearable accelerometers.”. Full article available here:
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RT @liyispatial: So happy to announce the first publication of exciting @nhs3 @HarvardGUTS Beiwe substudy which we collected massive volume….
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RT @jhubiostat: @JunruiDi, who received his PhD from @jhubiostat in 2019, is a Digital Medicine Statistician at Pfizer. Read more about Jun….
publichealth.jhu.edu
Junrui Di, PhD ’19, works at Pfizer as a digital medicine statistician.
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Proud to share our latest publication! Our team has developed a method to quantify disease progression in people with ALS using a single wrist-worn accelerometer. #ALSResearch #DrugDevelopment #DHTs #Wearables.
thelancet.com
Wearable accelerometer data can be used to generate digital biomarkers on upper limb movements and facilitate patient monitoring in free-living environments. The presented method offers interpretable...
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RT @patemedom: Hi friends! I’ve been busy working on a health tech startup, and we’re looking for our founding ML engineer. If you're passi….
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RT @calimagna: “We demonstrate that our method can estimate walking periods with high sensitivity and specificity: average sensitivity rang….
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RT @npjDigitalMed: Data from wearables can characterize daily physical activity and its change over time in persons with Amyotrophic Latera….
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RT @npjDigitalMed: New article! Read Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures ht….
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RT @npjDigitalMed: A single approach that can recognise walking with a variety of body-worn devices regardless of walking style, sensor loc….
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5. It is available as open-source software in MATLAB ( and Python (.
github.com
Forest is a library for analyzing smartphone-based high-throughput digital phenotyping data - onnela-lab/forest
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3. It’s computationally efficient, i.e., processes 7 days of raw data in a few seconds (well, depending on the processor and RAM). 4. May be used to quantify steps and cadence.
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1. Method may be applied to data collected at various body locations (thigh, waist, chest, arm, wrist) and it is sensor orientation independent. 2. It may be applied to raw accelerometer data collected with any sampling rate >= 10 Hz.
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RT @jponnela: LAST CALL: Openings in my group in Harvard biostatistics for postdocs in 1) network community detection and 2) methods for an….
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RT @jponnela: Openings for postdocs in my group! We're hiring in both areas: network science and digital phenotyping / smartphone data anal….
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RT @actigraph: Marta Karas from Harvard T.H. Chan School of Public Health presented her poster, “Wearable devices can track ALS disease pro….
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I’m excited to share that our paper on monitoring of medication adherence using smartphone-based digital phenotyping in patients with severe mental illness has just been published in Psychiatry Research. It is free to access at
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