Clinically deployable Kinesia technology for automated tremor assessment.

Area of Science:

• Biomedical Engineering
• Neurology
• Wearable Technology

Background:

• Parkinson's disease (PD) assessment relies on the subjective Unified Parkinson's Disease Rating Scale (UPDRS).
• Current PD evaluation methods lack objective, high-resolution kinematic data.
• There is a need for improved tools to monitor PD tremor severity and treatment efficacy.

Purpose of the Study:

• To design, build, and evaluate Kinesia, a wireless system for automated Parkinson's disease tremor assessment.
• To provide objective, high-resolution kinematic data for PD tremor.
• To improve the management and treatment evaluation of Parkinson's disease.

Main Methods:

• Developed Kinesia, a wearable system with accelerometers and gyroscopes.
• Collected kinematic data from PD subjects performing UPDRS tremor tasks.
• Processed quantitative kinematic features and developed a predictive mathematical model.

Main Results:

• Kinesia's kinematic features highly correlated with clinician scores for rest (r(2)=0.89), postural (r(2)=0.90), and kinetic (r(2)=0.69) tremor.
• A mathematical model accurately predicted tremor severity scores.
• PD subjects reported high clinical acceptance of the Kinesia system.

Conclusions:

• Kinesia offers an objective and reliable method for assessing Parkinson's disease tremor.
• The system has the potential to enhance the evaluation of treatment efficacy and patient management.
• Automated kinematic assessment can significantly advance PD care.