Deterioration of motor neurons due to amyotrophic lateral sclerosis (ALS) would affect the strides from one gait cycle to the next. Computer-assisted techniques are useful for gait analysis, and also have high potential in quantitatively monitoring the pathological progression. In this paper, we applied the signal turns count method to measure the fluctuations in the swing-interval time series recorded from 16 healthy control subjects and 13 patients with ALS. The swing-interval turns count (SWITC) parameter derived with the threshold of 0.06 s presented a significant difference (p < 0.001) between the healthy control subjects and ALS patients. Besides the SWITC, we also computed the averaged stride interval (ASI), which is usually longer in the patient with ALS (p < 0.0001), to characterize the gait patterns of ALS patients. In the pattern classification experiments, the Fisher's linear discriminant analysis (FLDA) and the least squares support vector machine (LS-SVM), both input with the SWITC and ASI features, were evaluated using the leave-one-out cross-validation method. The results showed that the LS-SVM with sigmoid kernels was able to provide a classification accurate rate of 89.66% and an area of 0.9629 under the receiver operating characteristic (ROC) curve, which were superior to those obtained with the linear classifier in the form of FLDA.

In this paper, an objective assessment for determining whether a person has Parkinson disease is proposed. This is achieved by analyzing the correlation between joint movements, since Parkinsonian patients often have trouble coordinating different joints in a movement. Thus, the auto-correlation coefficient of single joint movements and the cross-correlation between movements in a pair of joints (hand, wrist, elbow, and shoulder) were studied. These features were used to train and provide classification of subjects as having or not having Parkinson's disease using the least square support vector machine (LS-SVM). Experimental results showed that using either auto-correlation or cross-correlation features for classification provided over 91% correct classification. Using both features together provided better performance (96.0%) than using either feature alone. In addition, the performance of LS-SVM is better than that of self-organizing map (SOM) and k-nearest neighbor (KNN) in this case.

BACKGROUND There is a limited understanding about how gait speed influences the control of upper body motion during walking. Therefore, the primary purpose of this study was to examine how gait speed influences healthy individual's lower trunk motion during overground walking. The secondary purpose was to assess if Principal Component Analysis (PCA) can be used to gain further insight into postural responses that occur at different walking speeds. METHODS Thirteen healthy subjects (23 +/- 3 years) performed 5 straight-line walking trials at self selected slow, preferred, and fast walking speeds. Accelerations of the lower trunk were measured in the anterior-posterior (AP), vertical (VT), and mediolateral (ML) directions using a triaxial accelerometer. Stride-to-stride acceleration amplitude, regularity and repeatability were examined with RMS acceleration, Approximate Entropy and Coefficient of Multiple determination respectively. Coupling between acceleration directions were calculated using Cross Approximate Entropy. PCA was used to reveal the dimensionality of trunk accelerations during walking at slow and preferred speeds, and preferred and fast speeds. RESULTS RMS acceleration amplitude increased with gait speed in all directions. ML and VT trunk accelerations had less signal regularity and repeatability during the slow compared to preferred speed. However, stride-to-stride acceleration regularity and repeatability did not differ between the preferred and fast walking speed conditions, partly due to an increase in coupling between frontal plane accelerations. The percentage of variance accounted for by each trunk acceleration Principal Component (PC) did not differ between grouped slow and preferred, and preferred and fast walking speed acceleration data. CONCLUSION The main finding of this study was that walking at speeds slower than preferred primarily alters lower trunk accelerations in the frontal plane. Despite greater amplitudes of trunk acceleration at fast speeds, the lack of regularity and repeatability differences between preferred and fast speeds suggest that features of trunk motion are preserved between the same conditions. While PCA indicated that features of trunk motion are preserved between slow and preferred, and preferred and fast speeds, the discriminatory ability of PCA to detect speed-dependent differences in walking patterns is limited compared to measures of signal regularity, repeatability, and coupling.

Recent research work indicates that gait patterns are both non-linear and non-stationary signals and they can be analyzed using empirical mode decomposition. This paper describes gait pattern classification using features that are obtained by performing discrete cosine transforms (DCT) on intrinsic mode functions of five different human gait patterns. The DCT provides a compact 8-dimensional feature vector for gait pattern classification. Fifty two subjects participated in the experiment. The classification was performed using a Gaussian mixture model and an overall accuracy of 90.2% was achieved.

BACKGROUND Timely detection of early cognitive impairment is difficult. Measures taken in the clinic reflect a single snapshot of performance that might be confounded by the increased variability typical in aging and disease. We evaluated the use of continuous, long-term, and unobtrusive in-home monitoring to assess neurologic function in healthy and cognitively impaired elders. METHODS Fourteen older adults 65 years and older living independently in the community were monitored in their homes by using an unobtrusive sensor system. Measures of walking speed and amount of activity in the home were obtained. Wavelet analysis was used to examine variance in activity at multiple time scales. RESULTS More than 108,000 person-hours of continuous activity data were collected during periods as long as 418 days (mean, 315 +/- 82 days). The coefficient of variation in the median walking speed was twice as high in the mild cognitive impairment (MCI) group (0.147 +/- 0.074) as compared with the healthy group (0.079 +/- 0.027; t(11)= 2.266, P <.03). Furthermore, the 24-hour wavelet variance was greater in the MCI group (MCI, 4.07 +/- 0.14; healthy elderly, 3.79 +/- 0.23; F = 7.58, P </=.008), indicating that the day-to-day pattern of activity of subjects in the MCI group was more variable than that of the cognitively healthy controls. CONCLUSIONS The results not only demonstrate the feasibility of these methods but also suggest clear potential advantages to this new methodology. This approach might provide an improved means of detecting the earliest transition to MCI compared with conventional episodic testing in a clinic environment.

Trip related falls are a prevalent problem in the elderly. Early identification of at-risk gait can help prevent falls and injuries. The main aim of this study was to investigate the effectiveness of a wavelet based multiscale analysis of a gait variable [minimum foot clearance (MFC)] in comparison to MFC histogram plot analysis in extracting features for developing a model using support vector machines (SVMs) for screening of balance impairments in the elderly. MFC during walking on a treadmill was recorded on 13 healthy elderly and 10 elderly with a history of tripping falls. Features extracted from MFC histogram and then multiscale exponents between successive wavelet coefficient levels after wavelet decomposition of MFC series were used as inputs to the SVM to classify two gait patterns. The maximum accuracy of classification was found to be 100% for a SVM using a subset of selected wavelet based features, compared to 86.95% accuracy using statistical features. For estimating the relative risk of falls, the posterior probabilities of SVM outputs were calculated. These results suggest superior performance of SVM in the detection of balance impairments based on wavelet-based features and it could also be useful for evaluating for falls prevention intervention.

In this work, 33 dimensional time-frequency domain features were developed and evaluated to detect five different human walking patterns from data acquired using a triaxial accelerometer attached at the waist above the iliac spine. 52 subjects were asked to walk on a flat surface along a corridor, walk up and down a flight of a stairway and walk up and down a constant gradient slope, in an unsupervised manner. Time-frequency domain features of acceleration data in anterior-posterior (AP), medio-lateral (ML) and vertical (VT) direction were developed. The acceleration signal in each direction was decomposed to six detailed signals at different wavelet scales by using the wavelet packet transform. The rms values and standard deviations of the decomposed signals at scales 5 to 2 corresponding to the 0.78-18.75 Hz frequency band were calculated. The energies in the 0.39-18.75 Hz frequency band of acceleration signal in AP, ML and VT directions were also computed. The back-end of the system was a multi-layer perceptron (MLP) Neural Networks (NNs) classifier. Overall classification accuracies of 88.54% and 92.05% were achieved by using a round robin (RR) and random frame selecting (RFS) train-test method respectively for the five walking patterns.

The ability to rise from a chair is considered to be important to achieve functional independence and quality of life. This sit-to-stand task is also a good indicator to assess condition of patients with chronic diseases. We developed a wavelet based algorithm for detecting and calculating the durations of sit-to-stand and stand-to-sit transitions from the signal vector magnitude of the measured acceleration signal. The algorithm was tested on waist worn accelerometer data collected from young subjects as well as geriatric patients. The test demonstrates that both transitions can be detected by using wavelet transformation applied to signal magnitude vector. Wavelet analysis produces an estimate of the transition pattern that can be used to calculate the transition duration that further gives clinically significant information on the patients condition. The method can be applied in a real life ambulatory monitoring system for assessing the condition of a patient living at home.

Monitoring of posture change in sagittal plane and walking speed is important for evaluate the effectiveness of rehabilitation program or brace. We have developed a wearable device for monitoring human activity. However, in the previous system, there still remain several drawbacks for practical use such as accuracy in angle measurement, cumbersome cable arrangements, and so on. In order to improve these practical drawbacks, a new sensor system was designed, and its availability was evaluated. The results demonstrated that the accuracy of this system showed superior to that of the previous, and this system appears to be a significant means for quantitative assessment of the patient's motion.

A new method of monitoring physical activity that is able to detect walking upstairs using a miniature gyroscope attached to the shank is presented. Wavelet transformation, in conjunction with a simple kinematics model, was used to detect toe-off, heel-strike and foot-flat, as well cycles corresponding to stair ascent. To evaluate the system, three studies were performed. The method was first tested on 10 healthy young volunteer subjects in a gait laboratory where an ultrasonic motion system was used as a reference system. In the second study, the system was tested on three hospitalized elderly people to classify walking upstairs from walking downstairs and flat walking. In the third study, monitoring was performed on seven patients with peripheral vascular disease for 60min during their daily physical activity. The first study revealed a close relationship between the ambulatory and the reference systems. Compared to the reference system, the ambulatory system had an overall sensitivity and specificity of 98% and 97%, respectively. In the second study, the ambulatory system also showed a very high sensitivity (>94%) in identifying a 50 stairs ascent from walking on the flat and walking downstairs. Finally, compared with visual surveillance, we observed a relatively high accuracy in identifying 196 walking upstairs cycles through daily physical activity in the third study. Our results demonstrated a reliable technique of measuring walking upstairs during physical activity.

In this paper, we assess the complexity (fractal measure) of body motion during walking in patients with Parkinson's disease. The body motion of 11 patients with Parkinson's disease and 10 healthy elderly subjects was recorded using a triaxial accelerometry technique. A triaxial accelerometer was attached to the lumbar region. An assessment of the complexity of body motion was made using a maximum-likelihood-estimator-based fractal analysis method. Our data suggest that the fractal measures of the body motion of patients with Parkinson's disease are higher than those of healthy elderly subjects. These results were statistically different in the X (anteroposterior), Y (lateral) and Z (vertical) directions of body motion between patients with Parkinson's disease and the healthy elderly subjects (p < 0.01 in X and Z directions and p < 0.05 in Y direction). The complexity (fractal measure) of body motion can be useful to assess and monitor the output from the motor system during walking in clinical practice.

OBJECTIVE: We studied differences in the spatiotemporal dynamics of cortical oscillation across brain regions of patients with schizophrenia and normal subjects during the auditory oddball task using magnetoencephalography (MEG) and electroencephalography (EEG). METHODS: Ten right-handed male schizophrenia patients were studied. We used a newly developed adaptive spatial filtering algorithm optimized for robust source time-frequency reconstruction of MEG and EEG data, and obtained consecutive images in functional maps of event-related desynchronization (ERD) and synchronization (ERS) in theta, lower alpha (8-10 Hz), upper alpha (10-13 Hz), and beta bands. RESULTS: Beta ERD power at 750-1000 ms in patients was significantly increased in large right upper temporal and parietal regions and small upper portions of bilateral dorsal frontal and dorsal-medial parietal regions. Theta ERS power in schizophrenic patients during the oddball task was significantly increased in the left temporal pole at 250-500 ms, and was significantly increased in dorsal, medial frontal, and anterior portions of the anterior cingulate cortex in both hemispheres, and the left portion of lateral temporal regions at 500-750 ms, compared to the control group (family-wise error correction p<0.05). Lower alpha ERS power was significantly decreased in the right occipital region at 500-750 ms and in the right midline parietal and bilateral occipital regions at 750-1000 ms. Upper alpha ERS power was significantly decreased in right midline parietal and left occipital regions at 750-1000 ms. CONCLUSIONS: ERD/ERS changes were noted in the left temporal pole and midline frontal and anterior cingulate cortex in theta ERS, occipital lobe in alpha ERS, and right temporal-frontal-parietal, midline frontal, and anterior cingulate cortex in beta ERD. These findings may reflect disturbances in interaction among active large neuronal groups and their communication with each other that may be related to abnormal cognitive and psychopathological function. SIGNIFICANCE: Study of ERD and ERS by time-frequency analyses using MEG is useful to clarify data processing dysfunction in schizophrenia.

Anaerobic endurance training (AET) can improve sympathomimetic hyperactivity, and anaerobic interval training (AIT) is recommended for patients who cannot exercise due to exertional breathlessness and leg fatigue. However, the difference in sympathetic nerve activation (SNA) and parasympathetic nerve activation (PNA) during AIT and AET is unclear. The aim of this study is to investigate the differences between endurance and interval trainings. We studied three patients (63-73 years) assigned to AIT which exercise/pause phase is 60/120 seconds (AIT120) and AET of 10 minutes duration. Systolic blood pressure, heart rate (HR), and rate pressure product (as an index of SNA) and oxygen uptake, tidal volume, respiratory rate, and minute ventilation were measured. As a result, these parameters in AET were increased compared with those of AIT120 among the subjects. While, high frequency component of frequency distribution in HR (HF) in AET was decrease compared with that in AIT120 among subject. We concluded that AIT inhibited SNA more effectively compared with AET and AIT may be safe for cardiac rehabilitation.

The elderly can sometimes find rehabilitation training to be very boring, but if the participants are motivated or having fun, they will continue to exercise. Prevention is the most important issue for health care insurance in Japan, and since suitable training will improve the quality of life, we developed personal computer (PC)-based rehabilitation tools to help the elderly maintain balance and muscle strength. After using the balance-training device, the subjects were able to keep better balance, and the muscle-training device resulted in an energy expenditure of around 2 METs fewer than walking. The results indicate that PC-based rehabilitation tools are effective for maintaining physical exercise.

In this study, we evaluated the influence of floor materials on standing and walking in hemiplegic patients. To monitor body motion during standing and walking without any constraint, we used a measurement system that consisted of an accelerometer device, a telemeter system, and a personal computer. The posture angles in the antero-posterior and lateral directions were calculated from the low frequency component of the acceleration signal to evaluate body motion. Experiments were performed with six poststroke hemiplegic patients. We modified the time up and go test introduced by Podsiadle. The patients executed the task on three different floor materials: wooden flooring, linoleum, and carpet. The posture angle pattern on carpet differed from those on wooden flooring and linoleum. Therefore, the floor material influenced body motion. We suspect that this difference in movement corresponds to the hardness of the material.

We examined the periodic synchronous characteristic response to photic stimulation in schizophrenia using electroencephalography (EEG) and magnetoencephalography (MEG). We tested whether neural synchronization deficits were present in subjects with schizophrenia using photic stimulation to evaluate the frequency entrainment in 18 normal subjects and 19 schizophrenia patients. A conventional vertical-type 160-channel MEG (PQ1160C, Yokogawa Electric Corporation) was used. Photic stimulation was at frequencies from 8 to 10.5 Hz at intervals of 0.5 Hz. There were ten stimuli at each frequency, and each lasted 10 seconds. The power spectrum at each site was based on the international 10/20 derivation. The power spectrum in schizophrenia patients was smaller than that in normal subjects at each site. A gender difference was observed in normal subjects, but not in schizophrenia patients. MEG, like EEG, is an effective method for research on neuropathy of the psyche.

In rehabilitating stroke patients, many therapists use range of motion exercise (ROM-ex) at early post onset. There are three general types of ROM-ex: passive, active, and active-assistive ROM-ex is used to prevent joint contracture in paralyzed limbs and to assist in recovery of the central nervous system (CNS). However, its effect on CNS recovery is unclear. Therefore, this study compared the influence of different tasks, including passive and active ROM-ex and imagined extension/flexion at the elbow, on the cerebral cortex. The subjects were six healthy volunteers. We used a magnetoencephalogram (MEG) to measure cerebral cortex activity. In the active ROM-ex task, we confirmed a dipole in the motor area in all subjects. It has been suggested that this dipole is activity of the motor-related field (MRF). By contrast, in the passive ROM-ex experiment, we did not confirm a dipole in the cortex. In addition, in the experiment with no joint motion, in which the subject only imagined moving the elbow joint from flexion to extension, it was possible to estimate a dipole in the motor area. Therefore, an imaginary task might be a possible method of activation when voluntary movement is impossible.

PURPOSE The aim of this study was to investigate and improve the accuracy of accelerometer-type pedometers used by the elderly with slow walking speeds, with or without gait disorders, who do or do not use a cane. METHODS Eighteen subjects walked with a cane (5 males, 13 females; age, 80.9 +/- 7.7 yr; height, 148.1 +/- 7.7 cm; weight, 51.8 +/- 8.8 kg (mean +/- SD); nine had impaired gait), and 31 subjects walked without a cane (7 males, 24 females; age, 80.9 +/- 7.7 yr; height, 148.1 +/- 7.7 cm; weight, 51.8 +/- 8.8 kg; 15 had impaired gait). Subjects walked for approximately 20 m (10 m in each direction and a turning arc) at their own speed. We determined the number of steps by pedometer (PM), by visually counting the actual number of steps (RM), and by the triaxial acceleration signals. The power spectrum of the accelerometer in each direction calculated by fast Fourier transform (FFT) for a 4-s temporal window was normalized with the maximum power of each window. It was composited, and the frequency at maximum power was considered as the cadence. The number of steps taken (FM) was determined by summing all the estimated steps in each window. RESULTS PM was significantly less than the RM (P < 0.05), and the error of PM was 53.2 +/- 34.1% of RM. FM did not differ from the RM, and the average error of FM was -0.7 +/- 7.9% of RM (absolute value: 5.8 +/- 5.3%). CONCLUSION We suggest that our FFT method is suitable for estimating the number of steps during walking in this population.

Data mining techniques are highly useful in the study of various medical signals and images in order to obtain useful information to better predict the diagnosis or prognosis or treatment options for the patient. Study of the human walking pattern helps us understand the variability of motion during activities such as high performance walking and normal walking. A comparison of the parameters quantifying this variability in motion in normal young and elderly subjects and the subjects who need support will aid in better understanding of the relationship among walking patterns, age and disabilities. In this study, we measured the tri-axial acceleration along three directions: anteroposterior, lateral and vertical. We also measured gyrational pitch, roll and yaw. These parameters were obtained using sensors attached to the back, left thigh and right thigh of the three classes of subjects (normal, elderly and adults with support) during the three types of exercises: 10-m normal walk, 10-m high performance walk and stepping. These recorded signals were then subjected to wavelet packet decomposition, and three entropies, namely approximate entropy and two bispectral entropies, were obtained from the resultant wavelet coefficients. On analysing these entropies, we could observe the following:(1) the entropy steadily decreases with the increase in age and with the presence of impairments, and (2) the entropy decreases among all the three types of exercises, namely normal walking and high performance walking. We feel that the results of this work can help in the design of supporting devices for elderly subjects.

We investigated changes in blood pressure with exercise, including walking and ergometer training, sleep, and body weight. Blood pressure was monitored over a period of about 1 year in 61 subjects in Osaka, Japan. The morning systolic blood pressures were analyzed using multivariate regression analysis, and the correlations between systolic blood pressure and the above parameters were determined. The systolic blood pressure distribution was classified into improved, stable, and ingravescence groups. In the improved group, exercise intensity and total calories were important factors controlling the systolic blood pressure. More than 300 kcal per day was needed to improve the systolic blood pressure. In the stable and ingravescence groups, body weight control was also an important factor in maintaining blood pressure. An increase of 1 kg in body weight was associated with systolic blood pressure increases of 3 and 6 mm Hg in the stable and ingravescence groups, respectively. The long-term repeated use of home blood pressure testing may be a good self-care strategy for monitoring daily health.

BACKGROUND: The gait of healthy elderly and of subjects with Parkinson's disease (PD) displays some common features, suggesting that PD may be a model of ageing. AIM: The aim of the study was to quantify highlight the differences and similarities between the gait patterns of young PD and healthy elderly, to uncover if PD could be assumed as a model of ageing. DESIGN: An optoelectronic system was used for 3D gait analysis evaluation. POPULATION AND METHODS: We compared the gait parameters of 15 young PD (YPD) with the gait of 32 healthy elderly subjects (ES) and 21 healthy subjects age-matched with the PD subjects. RESULTS: Common features between YPD and ES were majorly found in the parameters that reflect the presence of an unstable, uncertain gait, and of corrective strategies employed to reduce instability. On the other side, typical features were present in the gait patterns of PD subjects. Conclusion. Our study helped identifying some typical characteristics of the onset disease, and to unravel the symptoms of ageing from those of PD by comparing young PD subjects to elderly healthy subjects. CLINICAL REHABILITATION IMPACT: This allows a deeper understanding of the mechanisms underlying the gait in ageing and PD.

The purpose of the present study is to evaluate accelerometric parameters of gait in different neurological conditions with pathological gait impairment compared to healthy subjects. We studied 17 patients affected by Parkinson's disease, 24 with ataxic gait due to different diseases and 24 healthy subjects supplied with a triaxial accelerometer with a portable datalogger which measures acceleration and deceleration on an anterior-posterior, mediolateral and vertical plane at an approximate level of the center of mass (back sacral localization) and in other two positions (sternal and frontal sacral region) during a steady-state walking. Analyses of the basic accelerometric parameters associated with a jerk analysis allowed us to differentiate between the population groups. We observed a significant reduction of acceleration parameters in neurological patients when compared with healthy subjects, with a reduction of the mean acceleration of 0.30 m/s(2) for ataxic and 0.64 m/s(2) for parkinsonian patients (t test, p < 0.01). The root-mean square of the accelerations was used to quantify the attenuations of accelerations. This study suggests that a triaxial accelerometer is a good practical and an economic tool for assessing the alteration of perambulation. Moreover, it is plausible to use these data to obtain objective parameters in the evaluation of the progression of the disease and the efficacy of therapeutic tools.

Data mining techniques are highly useful in the study of various medical signals and images in order to obtain useful information to better predict the diagnosis or prognosis or treatment options for the patient. Study of the human walking pattern helps us understand the variability of motion during activities such as high performance walking and normal walking. A comparison of the parameters quantifying this variability in motion in normal young and elderly subjects and the subjects who need support will aid in better understanding of the relationship among walking patterns, age and disabilities. In this study, we measured the tri-axial acceleration along three directions: anteroposterior, lateral and vertical. We also measured gyrational pitch, roll and yaw. These parameters were obtained using sensors attached to the back, left thigh and right thigh of the three classes of subjects (normal, elderly and adults with support) during the three types of exercises: 10-m normal walk, 10-m high performance walk and stepping. These recorded signals were then subjected to wavelet packet decomposition, and three entropies, namely approximate entropy and two bispectral entropies, were obtained from the resultant wavelet coefficients. On analysing these entropies, we could observe the following:(1) the entropy steadily decreases with the increase in age and with the presence of impairments, and (2) the entropy decreases among all the three types of exercises, namely normal walking and high performance walking. We feel that the results of this work can help in the design of supporting devices for elderly subjects.

OBJECTIVE To develop an automated and objective method to assess mobility in Parkinson disease (PD) patients in daily-life settings and to investigate whether accelerometer-derived measures discriminate between PD and healthy controls as they walk and simulate activities of daily living (ADL). METHODS Healthy older adults (17) and patients with PD (22) wore a triaxial accelerometer on their lower back during short walks (validation study) and during a walk around the medical center to simulate daily activities (ADL simulation). The variability (consistency and rhythmicity) of stepping was assessed. The patients completed the walks before and after taking their anti-Parkinsonian medications. Frequency-based acceleration measures included dominant frequency, amplitude (strength of signal frequency), width (frequency dispersion), and slope (a combination reflecting amplitude and width) of the main frequency of the power spectral density in the 0.5- to 3.0-Hz band. A subset of the Unified Parkinson-Disease Rating Scale provided a clinical measure of gait impairment (UPDRS-Gait5). A PD patient and control wore the sensors for 3 days at home. RESULTS The width was larger, and the amplitude and slope were smaller in the PD patients compared to the controls in the validation study and ADL simulation (P <.02). The width decreased, and the amplitude and slope increased when patients took anti-Parkinsonian medications (P <.007). Significant correlations were observed between acceleration-derived measures and UPDRS-Gait5. The data obtained at home was similar to the clinic data. CONCLUSIONS Frequency-derived measures are valid and sensitive estimates of stride-to-stride variability that can be used to assess the quality and consistency of walking in patients with PD in real-life settings.

Accelerometer-based activity monitoring sensors have become the most suitable means for objective assessment of mobility trends within patient study groups. The use of minimal, low power, IC (integrated circuit) components within these sensors enable continuous (long-term) monitoring which provides more accurate mobility trends (over days or weeks), reduced cost, longer battery life, reduced size and weight of sensor. Using scripted activities of daily living (ADL) such as sitting, standing, walking, and numerous postural transitions performed under supervised conditions by young and elderly subjects, the ability to discriminate these ADL were investigated using a single tri-axial accelerometer, mounted on the trunk. Data analysis was performed using Matlab® to determine the accelerations performed during eight different ADL. Transitions and transition types were detected using the scalar (dot) product technique and vertical velocity estimates on a single tri-axial accelerometer was compared to a proven discrete wavelet transform method that incorporated accelerometers and gyroscopes. Activities and postural transitions were accurately detected by this simplified low-power kinematic sensor and activity detection algorithm with a sensitivity and specificity of 86-92% for young healthy subjects in a controlled setting and 83-89% for elderly healthy subjects in a home environment.

The aim of this study was to extract multi-parametric measures characterizing different features of sit-to-stand (Si-St) and stand-to-sit (St-Si) transitions in older persons, using a single inertial sensor attached to the chest. Investigated parameters were transition's duration, range of trunk tilt, smoothness of transition pattern assessed by its fractal dimension, and trunk movement's dynamic described by local wavelet energy. A measurement protocol with a Si-St followed by a St-Si postural transition was performed by two groups of participants: the first group (N=79) included Frail Elderly subjects admitted to a post-acute rehabilitation facility and the second group (N=27) were healthy community-dwelling elderly persons. Subjects were also evaluated with Tinetti's POMA scale. Compared to Healthy Elderly persons, frail group at baseline had significantly longer Si-St (3.85±1.04 vs. 2.60±0.32, p=0.001) and St-Si (4.08±1.21 vs. 2.81±0.36, p=0.001) transition's duration. Frail older persons also had significantly decreased smoothness of Si-St transition pattern (1.36±0.07 vs. 1.21±0.05, p=0.001) and dynamic of trunk movement. Measurements after three weeks of rehabilitation in frail older persons showed that smoothness of transition pattern had the highest improvement effect size (0.4) and discriminative performance. These results demonstrate the potential interest of such parameters to distinguish older subjects with different functional and health conditions.

The purpose of this study was to investigate the influence of sport, age and different orthopeadical diseases on the variability of gait. 45 healthy, young subjects, 11 professional hand ball players, 24 patients after medial meniscectomy, 20 elderly, healthy subjects, and 20 patients with hip osteoarthritis were examined. The average, the standard deviation and the normalized deviation of spatial and temporal parameters were calculated for each person examined and in each group. Our data suggested that the normalized deviation of parameters enables the modelling of dynamic perception, because it is independent of parameter values due to normalization. Our tests show that the size of the parameter is independent of lateral dominance in healthy subjects. The magnitude of the coefficient of the variation of parameters depends on age, on the intensity of sports activities, and on orthopaedical diseases.

There is a lack of studies comparing the kinematics data of idiopathic Parkinson's disease (IPD) patients with healthy elder (HE) subjects, and when there is such research, it is not correlated to clinical measures. OBJECTIVE: To compare the spatio-temporal and kinematic parameters of Parkinsonian gait with the HE subjects group and measure the relation between these parameters and clinical instruments. METHOD: Twelve patients with IPD and fifteen HE subjects were recruited and evaluated for clinical instruments and gait analysis. RESULTS: There were statistically significant differences between HE group and the IPD group, in stride velocity, in stride length (SL), and in the hip joint kinematic data: on initial contact, on maximum extension during terminal contact and on maximum flexion during mid-swing. Regarding the clinical instruments there were significant correlated with in stride velocity and SL. CONCLUSION: Clinical instruments used did not present proper psychometric parameters to measure the IPD patient's gait, while the 3D system characterized it better.

We present an advanced method for feature extraction and cluster analysis of surface electromyograms (EMG) and acceleration signals in Parkinson's disease (PD). In the method, 12 different EMG and acceleration signal features are extracted and used to form high-dimensional feature vectors. The dimensionality of these vectors is then reduced by using the principal component approach. Finally, the cluster analysis of feature vectors is performed in a low-dimensional eigenspace. The method was tested with EMG and acceleration data of 42 patients with PD and 59 healthy controls. The obtained discrimination between patients and controls was promising. According to clustering results, one cluster contained 90% of the healthy controls and two other clusters 76% of the patients. Seven patients with severe motor dysfunctions were distinguished in one of the patient clusters. In the future, the clinical value of the method should be further evaluated.

BACKGROUND AND METHODS We used (123)I-Ioflupane SPECT to study 32 unrelated patients with essential tremor (16 with positive familial history), 47 sporadic tremor dominant patients with Parkinson's disease and 31 healthy control subjects. Discriminant analysis was used to categorize healthy subjects and patients with Parkinson's disease or essential tremor. RESULTS Patients with essential tremor had higher uptake values (50% putamen and 21% caudate, P<0.001) compared to those with Parkinson's disease but lower than healthy subjects (15% putamen and 21% caudate, P<0.05). Discriminant analysis classified seven essential tremor patients in the healthy subjects cohort (22%) and two as Parkinson's disease (6%). CONCLUSIONS Our results show that some essential tremor patients may present mild abnormalities of striatal dopamine transporters and a typical Parkinson's disease-like pattern of uptake loss. These findings suggest a link between the two disorders.