Parkinson’s disease is considered the fastest growing neurological disorder worldwide, and its global prevalence has doubled over the past 25 years according to the World Health Organization. Despite this growing prevalence, the disease is still primarily identified through visible motor symptoms such as tremor, slowed movements, or balance difficulties.
However, many physiological changes associated with Parkinson’s begin years before these symptoms become clearly noticeable. For this reason, researchers are increasingly investigating whether measurable physiological signals captured through wearable sensors could help reveal subtle motor alterations earlier than traditional clinical observation.
Parkinson’s disease and the challenge of early diagnosis
Parkinson’s disease (PD) is a progressive neurodegenerative disorder that primarily affects the motor system. The disease is associated with the gradual degeneration of dopamine-producing neurons in the brain, particularly in the substantia nigra, a region involved in regulating movement. As this degeneration progresses, individuals may develop characteristic motor symptoms such as resting tremor, bradykinesia (slowness of movement), muscular rigidity, and postural instability.
Although these symptoms are widely recognized, diagnosing PD, especially in its early stages, remains challenging. Motor symptoms often become clinically noticeable only after substantial neuronal loss has already occurred. Studies suggest that approximately 60–70% of dopaminergic neurons may already be lost by the time a clinical diagnosis is established.
Early identification of Parkinson’s disease is therefore an important objective in neurological research. This could help clinicians initiate therapeutic strategies sooner. However, many of the methods currently used to assess Parkinson’s symptoms rely largely on clinical observation rather than direct physiological measurement.
Why motor symptoms are difficult to quantify
Clinical evaluation of Parkinson’s disease commonly relies on standardized rating scales designed to assess the severity of motor impairment. One of the most widely used tools is the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Part III of this scale focuses specifically on motor assessment.
During these evaluations, patients perform a series of motor tasks including finger tapping, hand movements, posture assessment, and walking tests. Clinicians observe the performance and assign scores according to the severity of the symptoms they observe. Although these scales are essential in clinical practice, they also present limitations. Because the evaluation depends on human observation, scoring can be influenced by subjective interpretation.
Motor symptoms can also fluctuate throughout the day, particularly in patients undergoing pharmacological treatment. These challenges highlight the need for complementary approaches capable of describing motor impairments using measurable physiological signals rather than observation alone.
What digital biomarkers are
One concept gaining increasing attention in this context is the use of digital biomarkers. Digital biomarkers are objective and quantifiable physiological or behavioral measurements collected using digital devices such as sensors, wearable systems, or mobile technologies.
In Parkinson’s disease research, potential digital biomarkers may include characteristics such as tremor frequency, movement amplitude, gait variability, or patterns of muscle activation during specific motor tasks. When collected systematically, these signals can help researchers identify differences between individuals with Parkinson’s disease and healthy populations.
The use of quantitative physiological data also supports computational analysis. Signal processing and machine learning techniques can be applied to large datasets to identify patterns that may not be easily detectable through visual observation alone.
How wearables measure motor symptoms
Wearable biosignal technologies have become an important tool for capturing these physiological signals. Unlike traditional laboratory equipment, wearable sensors can be placed directly on the body, allowing researchers to measure movement and muscle activity during controlled tasks or natural movements.
Two complementary signal modalities are particularly relevant when studying Parkinson’s motor symptoms:
- Inertial Measurement Units (IMUs) are compact sensors capable of measuring acceleration, angular velocity, and orientation. This enables quantification of tremor amplitude, joint motion, gait dynamics, and postural stability.
- Electromyography (EMG) provides complementary information by measuring the electrical activity produced by skeletal muscles during contraction. EMG signals reveal how muscles are activated and coordinated during movement. Wearable EMG systems, such as muscleBAN BLE, make it possible to acquire muscle activity during structured motor tasks in a portable and unobtrusive experimental setup.
This type of setup is especially useful in clinical research protocols, human movement studies, and neuromuscular investigations, where researchers need high-quality physiological datasets collected under standardized conditions without overly constraining natural movement.
When combined, IMU and EMG data provide complementary insight into motor function. Motion sensors describe how the body moves, while EMG signals reveal how the neuromuscular system generates those movements.
Example research protocol
A practical example of this research approach can be found in the study protocol titled “Wearable Technology for Early Parkinson’s Disease Biomarker Identification – A Cross-Sectional Controlled Trial Protocol,” conducted by Júlia Rey Vilches and colleagues at the Technical University of Denmark (DTU) in collaboration with clinical partners.
The study investigates potential motor biomarkers by comparing 30 individuals diagnosed with Parkinson’s disease with 30 healthy control participants. Participants perform standardized motor tasks derived from the MDS-UPDRS Part III motor assessment, allowing researchers to evaluate tremor, coordination, movement speed, and posture under controlled conditions.
During these tasks, wearable sensors capture both kinematic movement data and muscle activity. Wearable EMG instrumentation such as muscleBAN supports the collection of portable muscle activation data during repeated motor assessments, complementing inertial measurements and contributing to a multimodal view of motor impairment.
By combining motion data and muscle activity signals, the researchers aim to identify measurable features that distinguish Parkinson’s-related motor patterns from those observed in healthy individuals. Recent findings suggest that analyzing muscle activity within specific standardized tasks can improve interpretability, as different movements capture distinct impairments such as rhythmic control or tremor-related activity.
Using features extracted from complementary tasks has also been shown to improve discrimination between Parkinson’s disease and healthy populations, indicating that different motor assessments provide non-overlapping and clinically relevant information.
These datasets may later support advanced computational analysis and the development of new digital biomarkers for neurological research.
Why standardized data collection matters
An important insight highlighted by this research is the limited availability of standardized protocols for collecting wearable physiological data in Parkinson’s studies. Differences in sensor placement, experimental tasks, or signal processing methods can make it difficult to compare results across studies. Establishing transparent and consistent data collection protocols is therefore essential for generating high-quality datasets that can support reproducible research and the development of reliable digital biomarkers.
Conclusion
As research into Parkinson’s disease continues to evolve, the ability to measure movement and muscle activity quantitatively is becoming increasingly important. Wearable biosignal technologies are allowing researchers to complement traditional clinical observation with objective physiological measurements. These tools are helping shift neurological research toward a more precise understanding of motor function and the physiological patterns that characterize disease.
