Wavelet-Based Tremor Quantification From Wrist-Worn Sensor Data in Home-Dwelling People With Parkinson's Disease

Tremor symptoms in Parkinson's disease (PD) are challenging to assess due to low resolution and subjectivity from standard clinical scales. To address this, wearable devices have been used, but algorithms have been relying on controlled or limited activity conditions. Our objective is to create a context-independent metric quantifying tremor in free-living conditions to bridge the gap between biomedical engineering and the PD field.

We designed an algorithm which computes a tremor index (TI) from accelerometer data, collected via the Empatica E4 worn on the wrist by home dwelling people with PD. For validation, we use a within-participant design, comparing the TIs of the most and least tremor-affected hand. We included seven participants with unilateral tremor, monitored for two weeks each. The algorithm is able to compute TIs for a set of frequencies identified in literature as associated with different tremor types (3-12 Hz), over adjustable sampling time windows.

We show that the most tremor-affected hand yields a higher TI than the other hand for frequency sets that are individual to each person, in particular around 5-6 Hz where rest tremor typically occurs. We find that we can disambiguate tremor across 3-12 Hz from general movement and resting states. The number of frequencies with inter-hand separation correlate with the MDS-UPDRS part III tremor items.

The designed tremor quantification algorithm can quantify tremor symptoms over time for people with PD and can be used to identify the individualized frequency ranges where these movements happen, in free-living conditions.