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Assessing the feasibility of using a commercially available bracelet to detect motor symptoms for home telemonitoring in patients with Parkinson’s disease


Marco Prenassi ,

Università degli studi di Trieste, IT
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Lorenzo Rossi,

Newronika s.r.l., Milan, IT
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Sara Marceglia

Università degli studi di Trieste, IT
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Introduction: Patients affected by Parkinson’s disease (PD) with deep brain stimulation (DBS) implants require a constant monitoring and support in a homecare environment. Aim of this work is to study the feasibility of an easy to use and commercially available wearable device capable of tracking the motor status of the patient and generate a time-based estimate of the bradykinesia during the daily activities.

Methods:The device used is a Pebble Time smart-watch. It includes a three-axis accelerometer. A dedicated app was developed to acquire data, and to provide a clinical diary to be filled in by the patient. An offline Matlab algorithm was implemented grounding on the current literature to evaluate the bradykinesia status. The system was tested on 3 patients (4 sessions) undergoing surgery for DBS electrode placement during a long-term monitoring with an external DBS device in an ecologic environment. The patient was clinically assessed using the Unified Parkinson’s Disease Rating Scale (UPDRS) part III, motor part that was correlated with the bradykinesia status obtained through the wearable equipment.

Results: The bracelet acquires the data with sample frequency in the 80-100 Hz range. One session of data collection lasts at least 4 hours. Every half an hour the app on the mobile phone asks the patient a simple questionnaire regarding the perceived motor status. At the end of the session, data is sent to a cloud service (e.g. Google Drive) and elaborated remotely.  The algorithm finds the Bradykinesia Accelerometric Score (BAS) in data bins of 4 minutes. The BAS algorithm was adapted from the patent of Griffiths & Horne [1]; the BAS is lower when the patient is bradykinetic. The scores were further analysed using a mean and variance changepoint analysis [2]. The Pearson correlation analysis showed that there is an inverse correlation (-0.701, p<0.004) between the clinical evaluation (UPDRS III) and the BAS.

Discussions and conclusions: Our results provide a time-based assessment of the bradykinesia or dyskinesia indexes (denoting ON-OFF states) during daily living activities of the PD patients usable in a homecare environment paired with a diary that assesses the perceived status of the patient. This assessment can be used to optimize drug treatment (i.e. L-DOPA) and DBS therapy over time.

Lessons learned:

- Patients are compliant and the device is well tolerated.

- The diary app in the mobile device helps the patient to keep track of his/her perceived status.

Limitations: The systems was tested during hospitalization and not in a normal homecare environment. Some data during the recording session is lost due to the relative low memory of the smart bracelet when it is not in close proximity to the mobile device (i.e. the patient wanders off without the phone).

Suggestions for future research: A weeklong recording session during normal daily living activities at home is required to foster the advancement of this research. In addition, a new algorithm to detect dyskinesia and tremor is required to fully describe the motor status of the patient.


1- Griffiths, Robert Irwin; HORNE, Malcolm Kenneth. Detection of hypokinetic and hyperkinetic states. U.S. Patent Application No 12/997,540, 2009.

2- Killick, Rebecca; Eckley, Idris. changepoint: An R package for changepoint analysis. Journal of Statistical Software, 2014, 58.3: 1-19.

3-Movement disorder society task force on rating scales for parkinson's disease, et al. The Unified Parkinson's Disease Rating Scale (UPDRS): status and recommendations. Movement disorders: official journal of the Movement Disorder Society, 2003, 18.7: 738.

How to Cite: Prenassi M, Rossi L, Marceglia S. Assessing the feasibility of using a commercially available bracelet to detect motor symptoms for home telemonitoring in patients with Parkinson’s disease. International Journal of Integrated Care. 2017;17(5):A584. DOI:
Published on 17 Oct 2017.


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