Kamiar Aminian

Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes

In this study we describe an ambulatory system for estimation of spatio-temporal parameters during long periods of walking. This original method based on wavelet analysis is proposed to compute the values of temporal gait parameters from the angular velocity of lower limbs. Based on a mechanical model, the medio-lateral rotation of the lower limbs during stance and swing, the stride length and velocity are estimated by integration of the angular velocity. Measurements accuracy was assessed using as a criterion standard the information provided by foot pressure sensors. To assess the accuracy of the method on a broad range of performance for each gait parameter, we gathered data from young and elderly subjects. No significant error was observed for toe-off detection, while a slight systematic delay (10 ms on average) existed between heelstrike obtained from gyroscopes and footswitch. There was no significant difference between actual spatial parameters (stride length and velocity) and their estimated values. Errors for velocity and stride length estimations were 0.06 m/s and 0.07 m, respectively. This system is light, portable, inexpensive and does not provoke any discomfort to subjects. It can be carried for long periods of time, thus providing new longitudinal information such as stride-to-stride variability of gait. Several clinical applications can be proposed such as outcome evaluation after total knee or hip replacement, external prosthesis adjustment for amputees, monitoring of rehabilitation progress, gait analysis in neurological diseases, and fall risk estimation in elderly.

Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring

An ambulatory gait analysis method using body-attached gyroscopes to estimate spatio-temporal parameters of gait has been proposed and validated against a reference system for normal and pathologic gait. Later, ten Parkinsons disease (PD) patients with subthalamic nucleus deep brain stimulation (STN-DBS) implantation participated in gait measurements using our device. They walked one to three times on a 20-m walkway. Patients did the test twice: once STN-DBS was ON and once 180 min after turning it OFF. A group of ten age-matched normal subjects were also measured as controls. For each gait cycle, spatio-temporal parameters such as stride length (SL), stride velocity (SV), stance (ST), double support (DS), and gait cycle time (GC) were calculated. We found that PD patients had significantly different gait parameters comparing to controls. They had 52% less SV, 60% less SL, and 40% longer GC. Also they had significantly longer ST and DS (11% and 59% more, respectively) than controls. STN-DBS significantly improved gait parameters. During the stim ON period, PD patients had 31% faster SV, 26% longer SL, 6% shorter ST, and 26% shorter DS. GC, however, was not significantly different. Some of the gait parameters had high correlation with Unified Parkinsons Disease Rating Scale (UPDRS) subscores including SL with a significant correlation (r=-0.90) with UPDRS gait subscore. We concluded that our method provides a simple yet effective way of ambulatory gait analysis in PD patients with results confirming those obtained from much more complex and expensive methods used in gait labs.

Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly

A new method of evaluating the characteristics of postural transition (PT) and their correlation with falling risk in elderly people is described. The time of sit-to-stand and stand-to-sit transitions and their duration were measured using a miniature gyroscope attached to the chest and a portable recorder placed on the waist. Based on a simple model and the discrete wavelet transform, three parameters related to the PT were measured, namely, the average and standard deviation of transition duration and the occurrence of abnormal successive transitions (number of attempts to have a successful transition). The comparison between two groups of elderly subjects (with high and low fall-risk) showed that the computed parameters were significantly correlated with the falling risk as determined by the record of falls during the previous year, balance and gait disorders (Tinetti score), visual disorders, and cognitive and depressive disorders (p < 0.01). In this study, the wavelet transform has provided a powerful technique for enhancing the pattern of PT, which was mainly concentrated into the frequency range of 0.04-0.68 Hz. The system is especially adapted for long-term ambulatory monitoring of elderly people.

Physical activity monitoring based on accelerometry: validation and comparison with video observation

The objective of this feasibility study is to evaluate the use of the ‘Physilog’ device, an ambulatory physical-activity recorder based on acceleration measurement, for the monitoring of daily physical activities. Accelerations measured at the level of the chest and the thigh are recorded by Physilog over a period of 1 h in five normal subjects. A specially designed studio-like room allowing the performance of most usual activities of everyday life is used. A video film synchronised with the Physilog is obtained for each subject to check the accuracy of the data derived from Physilog. Based on the analysis of the average and the deviation of the acceleration signal, an algorithm is developed to classify the activities in four categories, i.e. lying, sitting, standing and locomotion. Compared with the video observations, the results from the algorithm show an overall misclassification of 10.7%, which is mainly due to confusion between dynamic activities and the standing posture. In contrast, the misclassification between postures is negligible. It is concluded that Physilog can be used in the clinical setting for the reliable measurement and long-term recording of most-usual physical activities.

Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.

iTUG, a Sensitive and Reliable Measure of Mobility

Timed Up and Go (TUG) test is a widely used clinical paradigm to evaluate balance and mobility. Although TUG includes several complex subcomponents, namely: sit-to-stand, gait, 180° turn, and turn-to-sit; the only outcome is the total time to perform the task. We have proposed an instrumented TUG, called iTUG, using portable inertial sensors to improve TUG in several ways: automatic detection and separation of subcomponents, detailed analysis of each one of them and a higher sensitivity than TUG. Twelve subjects in early stages of Parkinsons disease (PD) and 12 age matched control subjects were enrolled. Stopwatch measurements did not show a significant difference between the two groups. The iTUG, however, showed a significant difference in cadence between early PD and control subjects (111.1 ± 6.2 versus 120.4 ± 7.6 step/min, p <; 0.006) as well as in angular velocity of arm-swing (123 ± 32.0 versus 174.0 ± 50.4°/s, p <; 0.005), turning duration (2.18 ± 0.43 versus 1.79 ± 0.27 s, p <; 0.023), and time to perform turn-to-sits (2.96 ± 0.68 versus 2.40 ± 0.33 s, p <; 0.023). By repeating the tests for a second time, the test-retest reliability of iTUG was also evaluated. Among the subcomponents of iTUG, gait, turning, and turn-to-sit were the most reliable and sit-to-stand was the least reliable.

Quantification of Tremor and Bradykinesia in Parkinson's Disease Using a Novel Ambulatory Monitoring System

An ambulatory system for quantification of tremor and bradykinesia in patients with Parkinsons disease (PD) is presented. To record movements of the upper extremities, a sensing units which included miniature gyroscopes, has been fixed to each of the forearms. An algorithm to detect and quantify tremor and another algorithm to quantify bradykinesia have been proposed and validated. Two clinical studies have been performed. In the first study, 10 PD patients and 10 control subjects participated in a 45-min protocol of 17 typical daily activities. The algorithm for tremor detection showed an overall sensitivity of 99.5% and a specificity of 94.2% in comparison to a video reference. The estimated tremor amplitude showed a high correlation to the Unified Parkinsons Disease Rating Scale (UPDRS) tremor subscore (e.g., r=0.87, p<0.001 for the roll axis). There was a high and significant correlation between the estimated bradykinesia related parameters estimated for the whole period of measurement and respective UPDRS subscore (e.g., r=-0.83, p<0.001 for the roll axis). In the second study, movements of upper extremities of 11 PD patients were recorded for periods of 3-5 hr. The patients were moving freely during the measurements. The effects of selection of window size used to calculate tremor and bradykinesia related parameters on the correlation between UPDRS and these parameters were studied. By selecting a window similar to the period of the first study, similar correlations were obtained. Moreover, one of the bradykinesia related parameters showed significant correlation (r=-0.74, p<0.01) to UPDRS with window sizes as short as 5 min. Our study provides evidence that objective, accurate and simultaneous assessment of tremor and bradykinesia can be achieved in free moving PD patients during their daily activities

A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes

A new method of measuring joint angle using a combination of accelerometers and gyroscopes is presented. The method proposes a minimal sensor configuration with one sensor module mounted on each segment. The model is based on estimating the acceleration of the joint center of rotation by placing a pair of virtual sensors on the adjacent segments at the center of rotation. In the proposed technique, joint angles are found without the need for integration, so absolute angles can be obtained which are free from any source of drift. The model considers anatomical aspects and is personalized for each subject prior to each measurement. The method was validated by measuring knee flexion-extension angles of eight subjects, walking at three different speeds, and comparing the results with a reference motion measurement system. The results are very close to those of the reference system presenting very small errors (rms=1.3, mean=0.2, SD=1.1 deg) and excellent correlation coefficients (0.997). The algorithm is able to provide joint angles in real-time, and ready for use in gait analysis. Technically, the system is portable, easily mountable, and can be used for long term monitoring without hindrance to natural activities.

The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson's disease

The Timed Up and Go (TUG) test has been used to assess balance and mobility in Parkinsons Disease (PD). However, it is not known if this test is sensitive to subtle abnormalities present in early stages of the disease, when balance and gait problems are not clinically evident but may be detected with instrumented analysis of movement. We hypothesise that postural transitions and arm swing during gait will be the most sensitive characteristics of the TUG for early PD. In the present study, we instrumented the TUG test (iTUG) using portable inertial sensors, and extended the walking distance from 3 m (traditional TUG) to 7 m. Twelve subjects with early-to-moderate, untreated PD and 12 healthy individuals participated. Our findings show that although the stopwatch measure of TUG duration did not detect any abnormalities in early-to-mid-stage PD, the peak arm swing velocity on the more affected side, average turning velocity, cadence and peak trunk rotation velocity were significantly slower. These iTUG parameters were also correlated with the Unified Parkinsons Disease Rating Motor Scale. Thus, the iTUG test is sensitive to untreated PD and could potentially detect progression of PD and response to symptomatic and disease-modifying treatments.

Estimation and visualization of sagittal kinematics of lower limbs orientation using body-fixed sensors

A new method of estimating lower limbs orientations using a combination of accelerometers and gyroscopes is presented. The model is based on estimating the accelerations of ankle and knee joints by placing virtual sensors at the centers of rotation. The proposed technique considers human locomotion and biomechanical constraints, and provides a solution to fusing the data of gyroscopes and accelerometers that yields stable and drift-free estimates of segment orientation. The method was validated by measuring lower limb motions of eight subjects, walking at three different speeds, and comparing the results with a reference motion measurement system. The results are very close to those of the reference system presenting very small errors (Shank: rms=1.0, Thigh: rms=1.6/spl deg/) and excellent correlation coefficients (Shank: r=0.999, Thigh: r=0.998). Technically, the proposed ambulatory system is portable, easily mountable, and can be used for long term monitoring without hindrance to natural activities. Finally, a gait analysis tool was designed to visualize the motion data as synthetic skeletons performing the same actions as the subjects.