Ambulatory monitoring of motor functions in patients with Parkinson's disease using kinematic sensors

Parkinson's disease (PD) is the second most common neurodegenerative disease in the general population. Cardinal symptoms of Parkinson's disease are resting tremor, rigidity, akinesia and bradykinesia and in advanced stages, gait impairments, postural instability and complications of chronic treatment with levodopa such as motor dysfunctions and dyskinesia. Multitude and complexity of these motor symptoms and their variability over the time have made assessment of them a difficult task. Moreover, following the fluctuations of motor performance (ON/OFF fluctuations) of the PD patients throughout their daily activities by quantifying their motor symptoms is a major challenge. The aim of this thesis was to design and validate a portable ambulatory movement analysis system for long-term monitoring and qualitative and quantitative assessment of motor abnormalities of PD patients during daily activities. We have designed a new measurement system consisting of five independent, lightweight, autonomous sensing units based on kinematic sensors that can continuously record body movements during daily life. Using this system and by performing several clinical studies, both in controlled conditions and on free moving patients, we have prepared a database of different movement patterns of PD patients. This database was the basis to design several new algorithms for the analysis of tremor, bradykinesia, gait and posture. An accurate algorithm based on spectral estimation has been proposed to detect and quantify tremor during daily activities of PD patients with a resolution down to three seconds using gyroscopes attached to the forearms. By quantifying the speed, range and the frequency of the movements, we have proposed a new method to assess the bradykinesia and tested it both in controlled and free conditions. We found out that in the free moving patients, the outcomes of this algorithm show significant and good correlation to the established clinical scores. Regarding the detection and analysis of gait, we have developed and tested a method based on four sensors attached to the lower limbs that provided spatio-temporal parameters of gait with good accuracy. We further improved our method using a new biomechanical model that could predict the movements of thighs from the movements of shanks during walking. This way we could reduce the number of sensor sites on the body while keeping the same accuracy in estimation of the spatio-temporal parameters of gait. By combining a statistical classifier, to detect transitions between sitting and standing postures, and a fuzzy classifier, to detect the basic body postures, we have developed an algorithm to classify basic body posture allocations both in PD patients and aged matched healthy subjects. Finally, while currently no other objective ambulatory method exists to accurately detect the periods of ON and OFF in PD patients, by combining the outcomes of the above algorithms (tremor, gait, bradykinesia and posture) using a statistical approach, we have proposed a method to detect periods of these two states with a resolution of 10 minutes in free moving patients. We believe that the proposed system has a high potential both for the clinical applications and research purposes related to the patient with Parkinson's disease and possibly other neurological movement disorders

Comparing the Mini-BESTest with the Berg Balance Scale to Evaluate Balance Disorders in Parkinson's Disease

Objective. The purpose of this study was to explore the usefulness of the Mini-BESTest compared to the Berg Balance Scale in evaluating balance in people with PD of varying severity. We evaluated (1) the distribution of patients scores to look for ceiling effects, (2) concurrent validity with severity of disease, and (3) the sensitivity/specificity of separating people with or without postural response deficits. Subjects. Ninety-seven people with PD were tested for balance deficits using the Berg, Mini-BESTest, Unified Parkinson's Disease Rating Scale (UPDRS) III and the Hoehn & Yahr (H&Y) disease severity classification. Setting. Clinical research facility at Oregon Health & Science University. Results. The Mini-BESTest is highly correlated with the Berg (r = 0.79, P < 0.001), but avoids the ceiling compression effect of the Berg for mild PD (skewness −2.30 Berg, −0.93 Mini-BESTest). Consequently, the Mini-BESTest is more effective than the Berg for predicting UPDRS Motor score (P < 0.001 Mini-BESTest versus P = 0.86 Berg), and for discriminating between those with and without postural response deficits as measured by the H&Y (ROC differential P = 0.06). Conclusion. The Mini-BESTest is a promising tool for discerning balance deficits in patients with PD, most importantly those with more subtle deficits

ISway: a sensitive, valid and reliable measure of postural control

BACKGROUND: Clinicians need a practical, objective test of postural control that is sensitive to mild neurological disease, shows experimental and clinical validity, and has good test-retest reliability. We developed an instrumented test of postural sway (ISway) using a body-worn accelerometer to offer an objective and practical measure of postural control. METHODS: We conducted two separate studies with two groups of subjects. Study I: sensitivity and experimental concurrent validity. Thirteen subjects with early, untreated Parkinson’s disease (PD) and 12 age-matched control subjects (CTR) were tested in the laboratory, to compare sway from force-plate COP and inertial sensors. Study II: test-retest reliability and clinical concurrent validity. A different set of 17 early-to-moderate, treated PD (tested ON medication), and 17 age-matched CTR subjects were tested in the clinic to compare clinical balance tests with sway from inertial sensors. For reliability, the sensor was removed, subjects rested for 30 min, and the protocol was repeated. Thirteen sway measures (7 time-domain, 5 frequency-domain measures, and JERK) were computed from the 2D time series acceleration (ACC) data to determine the best metrics for a clinical balance test. RESULTS: Both center of pressure (COP) and ACC measures differentiated sway between CTR and untreated PD. JERK and time-domain measures showed the best test-retest reliability (JERK ICC was 0.86 in PD and 0.87 in CTR; time-domain measures ICC ranged from 0.55 to 0.84 in PD and from 0.60 to 0.89 in CTR). JERK, all but one time-domain measure, and one frequency measure were significantly correlated with the clinical postural stability score (r ranged from 0.50 to 0.63, 0.01 < p < 0.05). CONCLUSIONS: Based on these results, we recommend a subset of the most sensitive, reliable, and valid ISway measures to characterize posture control in PD: 1) JERK, 2) RMS amplitude and mean velocity from the time-domain measures, and 3) centroidal frequency as the best frequency measure, as valid and reliable measures of balance control from ISway

A new measure of movement symmetry in early Parkinson's disease patients using symbolic processing of inertial sensor data

Movement asymmetry is one of the motor symptoms associated with Parkinson's Disease (PD). Therefore, being able to detect and measure movement symmetry is important for monitoring the patient's condition. The present paper introduces a novel symbol based symmetry index calculated from inertial sensor data. The method is explained, evaluated and compared to six other symmetry measures. These measures were used to determine the symmetry of both upper and lower limbs during walking of 11 early-to-mid-stage PD patients and 15 control subjects. The patients included in the study showed minimal motor abnormalities according to the Unified Parkinson's Disease Rating Scale (UPDRS). The symmetry indices were used to classify subjects into two different groups corresponding to PD or control. The proposed method presented high sensitivity and specificity with an area under the Receiver Operating Characteristic (ROC) curve of 0.872, 9\% greater than the second best method. The proposed method also showed an excellent Intraclass Correlation Coefficient (ICC) of 0.949, 55\% greater than the second best method. Results suggest that the proposed symmetry index is appropriate for this particular group of patients.©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.</p

Validity and reliability of an IMU-based method to detect APAs prior to gait initiation

Anticipatory postural adjustments (APAs) prior to gait initiation have been largely studied in traditional, laboratory settings using force plates under the feet to characterize the displacement of the center of pressure. However clinical trials and clinical practice would benefit from a portable, inexpensive method for characterizing APAs. Therefore, the main objectives of this study were (1) to develop a novel, automatic IMU-based method to detect and characterize APAs during gait initiation and (2) to measure its test-retest reliability. Experiment I was carried out in the laboratory to determine the validity of the IMU-based method in 10 subjects with PD (OFF medication) and 12 control subjects. Experiment II was carried out in the clinic, to determine test-retest reliability of the IMU-based method in a different set of 17 early-to-moderate, treated subjects with PD (tested ON medication) and 17 age-matched control subjects. Results showed that gait initiation characteristics (both APAs and 1st step) detected with our novel method were significantly correlated to the characteristics calculated with a force plate and motion analysis system. The size of APAs measured with either inertial sensors or force plate was significantly smaller in subjects with PD than in control subjects (p<. 0.05). Test-retest reliability for the gait initiation characteristics measured with inertial sensors was moderate-to-excellent (0.56. <. ICC. <. 0.82) for both groups.Our findings support the feasibility of automatically characterizing postural preparation and gait initiation with body-worn inertial sensors that would be practical for unsupervised clinical and home settings