ABSTRACT
Parkinson's disease (PD) is associated with alterations in motor outputs such as center of pressure (CoP) adjustments during quiet standing and foot kinematics during walking. Previous research suggests that the complexity of motor outputs reflects the number of control processes stabilizing a specific movement, providing a measure that is linked to the neurological control of the movement. The Entropic Half Life (EnHL) represents a new method for assessing motor output complexity. We hypothesized that there will be a lack of neuromuscular control pathways for PD patients, resulting in a decrease in motor output complexity. We computed the EnHL of CoP adjustments during quiet standing and foot kinematics during walking of 70 PD patients and 33 age-matched controls. Patients with PD showed longer EnHL values compared to controls, suggesting a tighter motor control. Excluding vision led to a decrease of EnHL of CoP in both groups. EnHL was correlated with spatio-temporal gait parameters. We compared EnHL with the pull test and the timed up-and-go test. No significant differences were present in the pull test, yet motor output complexity was correlated with the timed up-and-go test. The results suggest a reduced complexity in motor outputs of PD patients affecting distinct motor functions.
Subject(s)
Parkinson Disease/physiopathology , Walking/physiology , Aged , Biomechanical Phenomena , Case-Control Studies , Entropy , Female , Foot Joints/physiopathology , Gait/physiology , Humans , Male , Middle Aged , Movement/physiology , Posture/physiology , Pressure , Psychomotor Performance/physiologyABSTRACT
Parkinson's disease (PD) is characterized by progressive motor and non-motor symptoms, leading to distinct diagnostic and therapeutic challenges in all stages of the disease. This study investigated a mobile biosensor-based gait analysis system for patients in early and intermediate stages of PD compared to controls. Subjects wearing a motion sensor-equipped shoe performed a standardized gait exercise. Accelerometer- and gyroscope-based signals were analysed using a complex set of pattern recognition algorithms. The analysis was able (1) to distinguish between PD patients and controls, (2) to identify patients at an early stage of the disease and (3) to distinguish between early and intermediate stage patients. Thus, using this mobile biosensor-based analysis system we were able to obtain objective classifications of gait characteristics in PD. Future studies will show that mobile biosensor-based movement detection technology will support identification of early PD stages and allow objective characterization of motor fluctuations in advanced stages of the disease. This will provide an important and supportive tool for patients, caregivers and therapists.
