Fractal dynamics of body motion in patients with Parkinson's disease

Fractal Analysis in Neurodegenerative Diseases

Neurodegenerative diseases are defined by progressive nervous system dysfunction and death of neurons. The abnormal conformation and assembly of proteins is suggested to be the most probable cause for many of these neurodegenerative disorders, leading to the accumulation of abnormally aggregated proteins, for example, amyloid β (Aβ) (Alzheimer's disease and vascular dementia), tau protein (Alzheimer's disease and frontotemporal lobar degeneration), α-synuclein (Parkinson's disease and Lewy body dementia), polyglutamine expansion diseases (Huntington disease), or prion proteins (Creutzfeldt-Jakob disease). An aberrant gain-of-function mechanism toward excessive intraparenchymal accumulation thus represents a common pathogenic denominator in all these proteinopathies. Moreover, depending upon the predominant brain area involvement, these different neurodegenerative diseases lead to either movement disorders or dementia syndromes, although the underlying mechanism(s) can sometimes be very similar, and on other occasions, clinically similar syndromes can have quite distinct pathologies. Non-Euclidean image analysis approaches such as fractal dimension (FD) analysis have been applied extensively in quantifying highly variable morphopathological patterns, as well as many other connected biological processes; however, their application to understand and link abnormal proteinaceous depositions to other clinical and pathological features composing these syndromes is yet to be clarified. Thus, this short review aims to present the most important applications of FD in investigating the clinical-pathological spectrum of neurodegenerative diseases.

Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.

Gait metrics analysis utilizing single-point inertial measurement units: a systematic review

BACKGROUND

Wearable sensors, particularly accelerometers alone or combined with gyroscopes and magnetometers in an inertial measurement unit (IMU), are a logical alternative for gait analysis. While issues with intrusive and complex sensor placement limit practicality of multi-point IMU systems, single-point IMUs could potentially maximize patient compliance and allow inconspicuous monitoring in daily-living. Therefore, this review aimed to examine the validity of single-point IMUs for gait metrics analysis and identify studies employing them for clinical applications.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were followed utilizing the following databases: PubMed; MEDLINE; EMBASE and Cochrane. Four databases were systematically searched to obtain relevant journal articles focusing on the measurement of gait metrics using single-point IMU sensors.

RESULTS

A total of 90 articles were selected for inclusion. Critical analysis of studies was conducted, and data collected included: sensor type(s); sensor placement; study aim(s); study conclusion(s); gait metrics and methods; and clinical application. Validation research primarily focuses on lower trunk sensors in healthy cohorts. Clinical applications focus on diagnosis and severity assessment, rehabilitation and intervention efficacy and delineating pathological subjects from healthy controls.

DISCUSSION

This review has demonstrated the validity of single-point IMUs for gait metrics analysis and their ability to assist in clinical scenarios. Further validation for continuous monitoring in daily living scenarios and performance in pathological cohorts is required before commercial and clinical uptake can be expected.

Abnormal postural behavior in patients with functional movement disorders during exposure to stress

BACKGROUND

Patients affected by functional (psychogenic) movement disorders (FMD) have abnormal processing of stress responses. However, little is known about the influence of this abnormal stress processing on automatic motor defense behavior, such as freeze response. Our aim was thus to investigate stress-induced postural motor responses in FMD.

METHODS

Nine FMD patients and thirteen healthy controls were engaged in the Trier Social Stress Test, while we measured the movement of their body by means of accelerometers and gyroscopes attached to the thorax. Standard deviation of thorax acceleration, reflecting the variability of movement amplitude (body sway), was compared across groups over time in a 2 × 2 ANOVA design. Higuchi's fractal dimension (HFD), reflecting the complexity of movement pattern over time, was also analyzed. Salivary cortisol and α-amylase samples were collected before and after the experiment, as stress biomarkers. Pearson's correlation coefficients were calculated between these biomarkers and movement parameters.

RESULTS

A significant interaction effect was found, showing that healthy controls reduced their thorax sway over time during exposure to stress (from 0.027 ± 0.010 m/s² to 0.023 ± 0.008 m/s², effect size of Cohen's d = 0.95), whereas patients with FMD did not. This change in body sway in controls over time negatively correlated with salivary cortisol values (ρ = -0.67, p = 0.012). A significant group effect revealed that FMD patients had an overall larger body sway (0.038 ± 0.013 m/s²) compared to controls (0.025 ± 0.009 m/s² - effect size of Cohen's d = 1.29) and a lower HFD (1.602 ± 0.071) than controls (1.710 ± 0.078 - Cohen's d = 1.43).

CONCLUSIONS

Patients with FMD failed to show a reduction of body sway over time, i.e., freeze response observed in the controls, thus suggesting an impairment in the automatic defense behavior. Moreover, our analysis found a lower complexity of movement (HFD) in FMD, which deserves future research in order to verify whether this could represent a characteristic trait of the disorder.

Validity of shoe-type inertial measurement units for Parkinson's disease patients during treadmill walking

Background

When examining participants with pathologies, a shoe-type inertial measurement unit (IMU) system with sensors mounted on both the left and right outsoles may be more useful for analysis and provide better stability for the sensor positions than previous methods using a single IMU sensor or attached to the lower back and a foot. However, there have been few validity analyses of shoe-type IMU systems versus reference systems for patients with Parkinson’s disease (PD) walking continuously with a steady-state gait in a single direction. Therefore, the purpose of this study is to assess the validity of the shoe-type IMU system versus a 3D motion capture system for patients with PD during 1 min of continuous walking on a treadmill.

Methods

Seventeen participants with PD successfully walked on a treadmill for 1 min. The shoe-type IMU system and a motion capture system comprising nine infrared cameras were used to collect the treadmill walking data with participants moving at their own preferred speeds. All participants took anti-parkinsonian medication at least 3 h before the treadmill walk. An intraclass correlation coefficient analysis and the associated 95% confidence intervals were used to evaluate the validity of the resultant linear acceleration and spatiotemporal parameters for the IMU and motion capture systems.

Results

The resultant linear accelerations, cadence, left step length, right step length, left step time, and right step time showed excellent agreement between the shoe-type IMU and motion capture systems.

Conclusions

The shoe-type IMU system provides reliable data and can be an alternative measurement tool for objective gait analysis of patients with PD in a clinical environment.

Electronic supplementary material

The online version of this article (10.1186/s12984-018-0384-9) contains supplementary material, which is available to authorized users.

Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review

Background

Postural instability and gait disability threaten the independence and well-being of people with Parkinson’s disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson’s disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson’s disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population.

Methods

Papers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i) did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii) did not include a control group or control condition; iii) were an abstract and/or included in the proceedings of a conference; or iv) were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed to meet the inclusion criteria. Included articles were assessed for methodological quality and relevant data from the papers were extracted and synthesized.

Results

Quality assessment of these included articles indicated that 31% were of low methodological quality, while 58% were of moderate methodological quality and 11% were of high methodological quality. All studies adopted a cross-sectional design and used a variety of sensor types and outcome measures to assess standing balance or walking stability in people with Parkinson’s disease. Despite the typically low to moderate methodological quality, 81% of the studies reported differences in sensor-based measures of standing balance or walking stability between different groups of Parkinson’s disease patients and/or healthy controls.

Conclusion

These data support the use of wearable sensors for detecting differences in standing balance and walking stability between people with PD and controls. Further high-quality research is needed to better understand the utility of wearable sensors for the early identification of Parkinson’s disease symptoms and for assessing falls risk in this population.

PROSPERO Registration

CRD42014010838

Scale-free fluctuations in behavioral performance: delineating changes in spontaneous behavior of humans with induced sleep deficiency

The timing and dynamics of many diverse behaviors of mammals, e.g., patterns of animal foraging or human communication in social networks exhibit complex self-similar properties reproducible over multiple time scales. In this paper, we analyze spontaneous locomotor activity of healthy individuals recorded in two different conditions: during a week of regular sleep and a week of chronic partial sleep deprivation. After separating activity from rest with a pre-defined activity threshold, we have detected distinct statistical features of duration times of these two states. The cumulative distributions of activity periods follow a stretched exponential shape, and remain similar for both control and sleep deprived individuals. In contrast, rest periods, which follow power-law statistics over two orders of magnitude, have significantly distinct distributions for these two groups and the difference emerges already after the first night of shortened sleep. We have found steeper distributions for sleep deprived individuals, which indicates fewer long rest periods and more turbulent behavior. This separation of power-law exponents is the main result of our investigations, and might constitute an objective measure demonstrating the severity of sleep deprivation and the effects of sleep disorders.

Classification of exacerbation episodes in chronic obstructive pulmonary disease patients

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a progressive disease affecting the airways, which constitutes a major cause of chronic morbidity and a significant economic and social burden throughout the world. Despite the fact that in COPD patients exacerbations are common acute events causing significant and often fatal worsening of symptoms, an accurate prognostication continues to be difficult.

OBJECTIVES

To build computational models capable of distinguishing between normal life days from exacerbation days in COPD patients, based on physical activity measured by accelerometers.

METHODS

We recruited 58 patients suffering from COPD and measured their physical activity with accelerometers for 10 days or more, from August 2009 to March 2010. During this period we recorded six exacerbation episodes in the patients, accounting for 37 days. We were able to analyse data for 52 patients (369 patient days), and extracted three distinct sets of features from the data, one set of basic features such as average, one set based on the frequency domain and the last exploring the cross-information among sensors pairs. These were used by three machine-learning techniques (logarithmic regression, neural networks, support vector machines) to distinguish days with exacerbation events from normal days.

RESULTS

The support vector machine classifier achieved an AUC of 90% ± 9, when supplied with a set of features resulting from sequential feature selection method. Neu- ral networks achieved an AUC of 83% ± 16 and the logarithmic regression an AUC of 67% ± 15.

CONCLUSIONS

None of the individual feature sets provided robust for reasonable classification of PA recording days. Our results indicate that this approach has the potential to extract useful information for, but are not robust enough for medical application of the system.

A gait abnormality measure based on root mean square of trunk acceleration

Background

Root mean square (RMS) of trunk acceleration is seen frequently in gait analysis research. However, many studies have reported that the RMS value was related to walking speed. Therefore, the relationship between the RMS value and walking speed should be considered when the RMS value is used to assess gait abnormality. We hypothesized that the RMS values in three sensing axes exhibit common proportions for healthy people if they walk at their own preferred speed and that the RMS proportions in abnormal gait deviate from the common proportions. In this study, we proposed the RMS ratio (RMSR) as a gait abnormality measure and verified its ability to discriminate abnormal gait.

Methods

Forty-seven healthy male subjects (24–49 years) were recruited to examine the relationship between walking speed and the RMSR. To verify its ability to discriminate abnormal gait, twenty age-matched male hemiplegic patients (30–48 years) participated as typical subjects with gait abnormality. A tri-axial accelerometer was attached to their lower back, and they walked along a corridor at their own preferred speed. We defined the RMSR as the ratio between RMS in each direction and the RMS vector magnitude.

Results

In the healthy subjects, the RMS in all directions related to preferred walking speed. In contrast, RMSR in the mediolateral (ML) direction did not correlate with preferred walking speed (rs = -0.10, p = 0.54) and represented the similar value among the healthy subjects. Moreover, the RMSR in the ML direction for the hemiplegic patients was significantly higher than that for the healthy subjects (p < 0.01).

Conclusions

These results suggest that the RMSR in the ML direction exhibits a common value when healthy subjects walk at their own preferred speed, even if their preferred walking speed were different. For subjects with gait abnormality, the RMSR in the ML direction deviates from the common value of healthy subjects. The RMSR in the ML direction may potentially be a quantitative measure of gait abnormality.

Validity and inter-rater reliability of inertial gait measurements in Parkinson's disease: a pilot study

Walking models driven by centre of mass (CoM) data obtained from inertial measurement units (IMU) or optical motion capture systems (OMCS) can be used to objectively measure gait. However current models have only been validated within typical developed adults (TDA). The purpose of this study was to compare the projected CoM movement within Parkinson's disease (PD) measured by an IMU with data collected from an OMCS after which spatio-temporal gait measures were derived using an inverted pendulum model. The inter-rater reliability of spatio-temporal parameters was explored between expert researchers and clinicians using the IMU processed data. Participants walked 10 m with an IMU attached over their centre of mass which was simultaneously recorded by an OMCS. Data was collected on two occasions, each by an expert researcher and clinician. Ten people with PD showed no difference (p=0.13) for vertical, translatory acceleration, velocity and relative position of the projected centre of mass between IMU and OMCS data. Furthermore no difference (p=0.18) was found for the derived step time, stride length and walking speed for people with PD. Measurements of step time (p=0.299), stride length (p=0.883) and walking speed (p=0.751) did not differ between experts and clinicians. There was good inter-rater reliability for these parameters (ICC3.1=0.979, ICC3.1=0.958 and ICC3.1=0.978, respectively). The findings are encouraging and support the use of IMUs by clinicians to measure CoM movement in people with PD.

Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people

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.

Gait stability and variability measures show effects of impaired cognition and dual tasking in frail people

Background

Falls in frail elderly are a common problem with a rising incidence. Gait and postural instability are major risk factors for falling, particularly in geriatric patients. As walking requires attention, cognitive impairments are likely to contribute to an increased fall risk. An objective quantification of gait and balance ability is required to identify persons with a high tendency to fall. Recent studies have shown that stride variability is increased in elderly and under dual task condition and might be more sensitive to detect fall risk than walking speed. In the present study we complemented stride related measures with measures that quantify trunk movement patterns as indicators of dynamic balance ability during walking. The aim of the study was to quantify the effect of impaired cognition and dual tasking on gait variability and stability in geriatric patients.

Methods

Thirteen elderly with dementia (mean age: 82.6 ± 4.3 years) and thirteen without dementia (79.4 ± 5.55) recruited from a geriatric day clinic, walked at self-selected speed with and without performing a verbal dual task. The Mini Mental State Examination and the Seven Minute Screen were administered. Trunk accelerations were measured with an accelerometer. In addition to walking speed, mean, and variability of stride times, gait stability was quantified using stochastic dynamical measures, namely regularity (sample entropy, long range correlations) and local stability exponents of trunk accelerations.

Results

Dual tasking significantly (p < 0.05) decreased walking speed, while stride time variability increased, and stability and regularity of lateral trunk accelerations decreased. Cognitively impaired elderly showed significantly (p < 0.05) more changes in gait variability than cognitive intact elderly. Differences in dynamic parameters between groups were more discerned under dual task conditions.

Conclusions

The observed trunk adaptations were a consistent instability factor. These results support the concept that changes in cognitive functions contribute to changes in the variability and stability of the gait pattern. Walking under dual task conditions and quantifying gait using dynamical parameters can improve detecting walking disorders and might help to identify those elderly who are able to adapt walking ability and those who are not and thus are at greater risk for falling.

Correlation among joint motions allows classification of Parkinsonian versus normal 3-D reaching

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.

Computer-aided analysis of gait rhythm fluctuations in amyotrophic lateral sclerosis

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.

Lower trunk motion and speed-dependence during walking

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.

Accelerometry: a technique for quantifying movement patterns during walking

The popularity of using accelerometer-based systems to quantify human movement patterns has increased in recent years for clinicians and researchers alike. The benefits of using accelerometers compared to more traditional gait analysis instruments include low cost; testing is not restricted to a laboratory environment; accelerometers are small, therefore walking is relatively unrestricted; and direct measurement of 3D accelerations eliminate errors associated with differentiating displacement and velocity data. However, accelerometry is not without its disadvantages, an issue which is scarcely reported in gait analysis literature. This paper reviews the use of accelerometer technology to investigate gait-related movement patterns, and addresses issues of acceleration measurement important for experimental design. An overview of accelerometer mechanics is provided before illustrating specific experimental conditions necessary to ensure the accuracy of gait-related acceleration measurement. A literature review is presented on how accelerometry has been used to examine basic temporospatial gait parameters, shock attenuation, and segmental accelerations of the body during walking. The output of accelerometers attached to the upper body has provided useful insights into the motor control of normal walking, age-related differences in dynamic postural control, and gait patterns in people with movement disorders.

Power-law temporal autocorrelation of activity reflects severity of parkinsonism

We aimed to obtain a reliable, objective scale representing disease severity for appropriate management of patients with Parkinson's disease (PD). Nineteen patients with PD at the Department of Neurology, Tokyo University Hospital, were classified into mild (n = 10) or severe groups (n = 9) depending on their Hoehn-Yahr scores, and wore accelerometers on their wrists for more than 6 consecutive days. During this time we monitored their subjective assessments of symptom severity and analyzed the power-law exponents (alpha) for local maxima and minima of fluctuations in the activity time series. Statistical comparisons were made between the severe and mild groups and of individual patients on "good condition" and "bad condition" days, as well as between days before and after antiparkinsonism medication. In all patients, the alpha for local maxima was always lower when parkinsonism was mild than when severe. Presence of tremor did not influence the alpha for local maxima. As the lower alpha value for local maxima of fluctuations in activity records reflects more frequent switching behavior from low to high physical activities or the severity of akinesia, actigraph monitoring of parkinsonism, and analysis of its power-law correlation may provide useful objective information for controlling parkinsonism in outpatient clinics and for evaluating new antiparkinsonism drugs.

Fractal dynamics of body motion in post-stroke hemiplegic patients during walking

In this paper, we quantify the complexity of body motion during walking in post-stroke hemiplegic patients. The body motion of patients and healthy elderly subjects was measured by using the accelerometry technique. The complexity of body motion was quantified using the maximum likelihood estimator (MLE-) based fractal analysis methods. Our results suggest that the fractal dimensions of the body motion in post-stroke hemiplegic patients at several Brunnstrom stages were significantly higher than those of healthy elderly subjects (p < 0.05). However, in the hemiplegic patients, the fractal dimensions were more related to Brunnstrom stages.