Quantitative Analysis of Motor Status in Parkinson's Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications.

Long-term and objective monitoring is necessary for full assessment of the condition of patients with Parkinson's disease (PD). Recent advances in biotechnology have seen the development of various types of wearable (body-worn) sensor systems. By using accelerometers and gyroscopes, these devices can quantify motor abnormalities, including decreased activity and gait disturbances, as well as nonmotor signs, such as sleep disturbances and autonomic dysfunctions in PD. This review discusses methodological problems inherent in wearable devices. Until now, analysis of the mean values of motion-induced signals on a particular day has been widely applied in the clinical management of PD patients. On the other hand, the reliability of these devices to detect various events, such as freezing of gait and dyskinesia, has been less than satisfactory. Quantification of disease-specific changes rather than nonspecific changes is necessary.

Whole-Day Gait Monitoring in Patients with Alzheimer's Disease: A Relationship between Attention and Gait Cycle.

Background: Gait impairment in patients with Alzheimer's disease (AD) and its relationship with cognitive function has been described, but reports of gait analysis in AD in daily living are limited. Objective: To investigate whether gait pattern of patients with AD in daily living is associated with cognitive function. Methods: Gait was recorded in 24 patients with AD and 9 healthy controls (HC) for 24 hours by using a portable gait rhythmogram. Mean gait cycle and gait acceleration were compared between the AD and HC groups. For the AD group, these gait metrics were assessed for correlations with cognitive function, as determined by the Mini Mental State Examination and Wechsler Memory Scale-Revised (WMS-R). Results: Although both gait parameters were not different between the patients with AD and HC, gait cycle in patients with AD was positively correlated with attention/concentration scores on the WMS-R (r = 0.578), and not with memory function. Patients with AD with attention scores as high as HC displayed a longer gait cycle than both HC (p = 0.048) and patients with AD with lower attention scores (p = 0.011). The patients with AD with lower attention scores showed a similar gait cycle with HC (p = 0.994). Conclusion: Patients with AD with impaired attentional function walk with faster gait cycle comparable to HC in daily living walking, which was unexpected based on previous gait analysis in clinical settings. This result probably reflects diminished consciousness to either the environment or instability of gait in the patients with AD with impaired attention.

Ambulatory Gait Behavior in Patients With Dementia: A Comparison With Parkinson's Disease.

Accelerometry-based gait analysis is a promising approach in obtaining insightful information on the gait characteristics of patients with neurological disorders such as dementia and Parkinson's disease (PD). In order to improve its practical use outside the laboratory or hospital, it is required to design new metrics capable of quantifying ambulatory gait and their extraction procedures from long-term acceleration data. This paper presents a gait analysis method developed for such a purpose. Our system is based on a single trunk-mounted accelerometer and analytical algorithm for the assessment of gait behavior that may be context dependent. The algorithm consists of the detection of gait peaks from acceleration data and the analysis of multimodal patterns in the relationship between gait cycle and vertical gait acceleration. A set of six new measures can be obtained by applying the algorithm to a 24-h motion signal. To examine the performance and utility of our method, we recorded acceleration data from 13 healthy, 26 PD, and 26 mild cognitive impairment or dementia subjects. Each patient group was further classified into two, comprising 13 members each, according to the severity of the disease, and the gait behavior of the five groups was compared. We found that the normal, PD, and MCI/dementia groups show characteristic walking patterns which can be distinguished from one another by the developed gait measure set. We also examined conventional parameters such as gait acceleration, gait cycle, and gait variability, but failed to reproduce the distinct differences among the five groups. These findings suggest that the proposed gait analysis may be useful in capturing disease-specific gait features in a community setting.

Quantitative evaluation of gait ataxia by accelerometers.

An appropriate biomarker for spinocerebellar degeneration (SCD) has not been identified. Here, we performed gait analysis on patients with pure cerebellar type SCD and assessed whether the obtained data could be used as a neurophysiological biomarker for cerebellar ataxia. We analyzed 25 SCD patients, 25 patients with Parkinson's disease as a disease control, and 25 healthy control individuals. Acceleration signals during 6 min of walking and 1 min of standing were measured by two sets of triaxial accelerometers that were secured with a fixation vest to the middle of the lower and upper back of each subject. We extracted two gait parameters, the average and the coefficient of variation of motion trajectory amplitude, from each acceleration component. Then, each component was analyzed by correlation with the Scale for the Assessment and Rating of Ataxia (SARA) and the Berg Balance Scale (BBS). Compared with the gait control of healthy subjects and concerning correlation with severity and disease specificity, our results suggest that the average amplitude of medial-lateral (upper back) of straight gait is a physiological biomarker for cerebellar ataxia. Our results suggest that gait analysis is a quantitative and concise evaluation scale for the severity of cerebellar ataxia.

Kinematic analysis of 24-hour recording of walking pattern in patients with vascular parkinsonism.

BACKGROUND: There is a need to define the basic characteristics of various kinematic parameters recorded during walking in patients with vascular parkinsonism (VP). The present study was designed to determine the kinematic features of walking in VP patients. For this purpose, gait acceleration and gait cycle were recorded continuously over 24-h period of daily living in VP patients, patients with Parkinson's disease (PD), and healthy subjects. METHODS: We used our newly developed 24-h monitoring device, the portable gait rhythmogram, which records gait during walking, and computes gait-induced accelerations with pattern matching algorithm. We studied nine VP patients with history of multiple lacunar infarcts (mean age ± standard deviation (SD): 72.6 ± 5.0 years, 7 men), 39 PD patients (mean age ± SD: 70.8 ± 5.8 years, 18 women), and 15 normal control subjects (mean age ± SD: 67.9 ± 4.7 years, 9 men). RESULTS: The "amount of overall movements per 24 h" was lower in VP and PD, compared with the control, with no significant differences between the two groups. Gait acceleration during walking was significantly lower (p < 0.01 in each case), while the gait cycle was the same in VP and PD patients compared with the control. CONCLUSIONS: The results suggest that deficit in force production and preservation of gait rhythm are common features of walking patterns in VP and PD patients.

Visualising gait symmetry/asymmetry from acceleration data.

Accelerometry-based quantification of gait symmetry/asymmetry is a promising approach for objectively evaluating gait dysfunctions. An important step in the application of this method in clinical settings is to develop reliable gait asymmetry measures and tools for visualising them to create easy-to-understand presentations for both clinicians and patients. This paper describes a new self-adaptive algorithm for estimating motion trajectory from acceleration data and visualising the degree of its asymmetry in 3D space. Two new parameters are introduced to capture asymmetric walking patterns based on the assessment of 3D autocorrelation and biphasicity of the motion trajectory. The performance of our algorithm is confirmed by analysing gait data collected from 245 healthy subjects. The proposed method may be clinically useful in tracking the process of recovering from pathology or injury after rehabilitation.

Human turnover dynamics during sleep: statistical behavior and its modeling.

Turnover is a typical intermittent body movement while asleep. Exploring its behavior may provide insights into the mechanisms and management of sleep. However, little is understood about the dynamic nature of turnover in healthy humans and how it can be modified in disease. Here we present a detailed analysis of turnover signals that are collected by accelerometry from healthy elderly subjects and age-matched patients with neurodegenerative disorders such as Parkinson's disease. In healthy subjects, the time intervals between consecutive turnover events exhibit a well-separated bimodal distribution with one mode at ⩽10 s and the other at ⩾100 s, whereas such bimodality tends to disappear in neurodegenerative patients. The discovery of bimodality and fine temporal structures (⩽10 s) is a contribution that is not revealed by conventional sleep recordings with less time resolution (≈30 s). Moreover, we estimate the scaling exponent of the interval fluctuations, which also shows a clear difference between healthy subjects and patients. We incorporate these experimental results into a computational model of human decision making. A decision is to be made at each simulation step between two choices: to keep on sleeping or to make a turnover, the selection of which is determined dynamically by comparing a pair of random numbers assigned to each choice. This decision is weighted by a single parameter that reflects the depth of sleep. The resulting simulated behavior accurately replicates many aspects of observed turnover patterns, including the appearance or disappearance of bimodality and leads to several predictions, suggesting that the depth parameter may be useful as a quantitative measure for differentiating between normal and pathological sleep. These findings have significant clinical implications and may pave the way for the development of practical sleep assessment technologies.

Accelerometry-based gait analysis and its application to Parkinson's disease assessment--part 1: detection of stride event.

Gait analysis is widely recognized as a promising tool for obtaining objective information on the walking behavior of Parkinson's disease (PD) patients. It is especially useful in clinical practices if gait properties can be captured with minimal instrumentation that does not interfere with the subject's usual behavioral pattern under ambulatory conditions. In this study, we propose a new gait analysis system based on a trunk-mounted acceleration sensor and automatic gait detection algorithm. The algorithm identifies the acceleration signal with high intensity, periodicity, and biphasicity as a possible gait sequence, from which gait peaks due to stride events are extracted by utilizing the cross-correlation and anisotropy properties of the signal. A total of 11 healthy subjects and 12 PD patients were tested to evaluate the performance of the algorithm. The result indicates that gait peaks can be detected with an accuracy of more than 94%. The proposed method may serve as a practical component in the accelerometry-based assessment of daily gait characteristics.

A study of gait acceleration and synchronisation in healthy adult subjects.

Accelerometry-based gait analysis is widely recognised as a promising tool in healthcare and clinical settings since it is unobtrusive, inexpensive and capable of providing insightful information on human gait characteristics. In order to expand the application of this technology in daily environments, it is desirable to develop reliable gait measures and their extraction methods from the acceleration signal that can differentiate between normal and atypical gait. Important examples of such measures are gait cycle and gait-induced acceleration magnitude, which are known to be closely related to each other depending on each individual's physical condition. In this study, we derive a model equation with two parameters which captures the essential relationships between gait cycle and gait acceleration based on experiments and physical modelling. We also introduce as a new gait parameter a set of indexes to evaluate the synchronisation behaviour of gait timing. The function and utility of the proposed parameters are examined in 11 healthy subjects during walking under various selected conditions.

Accelerometry-based gait analysis and its application to Parkinson's disease assessment- part 2: a new measure for quantifying walking behavior.

Gait analysis is a valuable tool for obtaining quantitative information on motor deficits in Parkinson's disease (PD). Since the characteristic gait patterns of PD patients may not be fully identified by brief examination in a clinic, long-term, and unobtrusive monitoring of their activities is essential, especially in a nonclinical setting. This paper describes a single accelerometer-based gait analysis system for the assessment of ambulatory gait properties. Acceleration data were recorded continuously for up to 24 h from normal and PD subjects, from which gait peaks were picked out and the relationship between gait cycle and vertical gait acceleration was evaluated. By fitting a model equation to the relationships, a quantitative index was obtained for characterizing the subjects' walking behavior. The averaged index for PD patients with gait disorder was statistically smaller than the value for normal subjects. The proposed method could be used to evaluate daily gait characteristics and thus contribute to a more refined diagnosis and treatment of the disease.

Deficits in scaling of gait force and cycle in parkinsonian gait identified by long-term monitoring of acceleration with the portable gait rhythmogram.

To examine the range of gait acceleration and cycle in daily walking of patients with Parkinson's disease (PD), we compared the gait of 40 patients with PD and 17 normal controls by using a newly developed long-term monitoring device that extracts gait-related accelerations from overall movements-related accelerations. The range of change in gait acceleration, relative to the control, was less than 75% in 12 patients. The range of change in gait cycle was less than 75% in 8 patients. The range of changes in both parameters was less than 75% in 4 patients. The results suggest narrow changes in gait parameters in PD.

Quantitative assessment of gait bradykinesia in Parkinson's disease using a portable gait rhythmogram.

To quantify gait bradykinesia during daily activity in patients with Parkinson's disease (PD), we measured movement-induced accelerations over more than 24h in 50 patients with PD and 17 age-matched normal controls, using a new device, the portable gait rhythmogram. Acceleration values induced by various movements, averaged each 10 min, exhibited a gamma distribution. The mean value of the distribution curve was used as an index of the "amount of overall movement per 24h". Characteristic changes were observed in both the gait cycle and gait acceleration. During hypokinesia, the gait cycle became either faster or slower. A number of patients with marked akinesia/bradykinesia showed a reduced and narrow range of gait acceleration, i.e., a range of floor reaction forces. The results suggest that assessment of the combination of changes in gait cycle and gait acceleration can quantitatively define the severity of gait bradykinesia.

How far do the complaints of patients with Parkinson's disease reflect motor fluctuation? Quantitative analysis using a portable gait rhythmogram.

In advanced-stage Parkinson's disease (PD), motor fluctuation is a frequent and disabling problem. Assessment of motor fluctuation depends on patient's subjective self-statement. We examined whether the subjective fluctuation matched the objective motor fluctuation defined by gait disorders. Using a new device, the portable gait rhythmogram, we recorded gait cadence and acceleration continuously over the 24-hour period in 54 patients with PD and 17 normal controls, for the quantitative evaluation of motor fluctuation. The patients were asked to estimate motor fluctuation every hour. In 44 of 54 patients, changes in the cadence were associated with simultaneous changes in acceleration. We examined the subjective fluctuation in these 44 patients who were confirmed to have motor fluctuation. Nineteen (82.7%) of 23 patients who felt no fluctuation showed distinct gait disorders. During off time, they walked with marked short or bradykinetic stepping. No matching changes were observed in either the cadence or acceleration in 11 (52.4%) of 21 patients who perceived motor fluctuation. No synchronization was noted in 30 (68.2%) of the 44 patients, between the times of subjectively assessed motor fluctuation and those of quantitative analysis of gait disorder. This discrepancy suggests that the objective continuous recording of the cadence and acceleration is necessary to understand motor fluctuation.

24-hour recording of parkinsonian gait using a portable gait rhythmogram.

OBJECTIVE: In the advanced stage of Parkinson's disease (PD), motor fluctuation is a frequent and a disabling problem. Despite its importance, motor fluctuation has received little scientific analysis probably due to limitation in objective assessment. Here, we focused on gait disorders to estimate motor fluctuation in daily activities. PATIENTS AND METHODS: Using a new device, the portable gait rhythmogram, we recorded gait rhythm continuously over 24 hours in 22 patients with PD and in 11 normal controls, for quantitative evaluation of motor fluctuation. The duration of one gait cycle was measured. RESULTS: Continuous 24-hour recording identified changes in gait rhythm, which correlated with fluctuation of PD symptoms. Different motor fluctuations were observed; a shift to a faster gait cycle was noted in patients with short-step walking, festination or freezing of gait, whereas a shift to a slower gait cycle was observed in patients with bradykinesia or instability. CONCLUSION: Characterization of motor fluctuation using this device could help in the selection of appropriate anti-PD medications.

Effect of extreme data loss on long-range correlated and anticorrelated signals quantified by detrended fluctuation analysis.

Detrended fluctuation analysis (DFA) is an improved method of classical fluctuation analysis for nonstationary signals where embedded polynomial trends mask the intrinsic correlation properties of the fluctuations. To better identify the intrinsic correlation properties of real-world signals where a large amount of data is missing or removed due to artifacts, we investigate how extreme data loss affects the scaling behavior of long-range power-law correlated and anticorrelated signals. We introduce a segmentation approach to generate surrogate signals by randomly removing data segments from stationary signals with different types of long-range correlations. The surrogate signals we generate are characterized by four parameters: (i) the DFA scaling exponent alpha of the original correlated signal u(i) , (ii) the percentage p of the data removed from u(i) , (iii) the average length mu of the removed (or remaining) data segments, and (iv) the functional form P(l) of the distribution of the length l of the removed (or remaining) data segments. We find that the global scaling exponent of positively correlated signals remains practically unchanged even for extreme data loss of up to 90%. In contrast, the global scaling of anticorrelated signals changes to uncorrelated behavior even when a very small fraction of the data is lost. These observations are confirmed on two examples of real-world signals: human gait and commodity price fluctuations. We further systematically study the local scaling behavior of surrogate signals with missing data to reveal subtle deviations across scales. We find that for anticorrelated signals even 10% of data loss leads to significant monotonic deviations in the local scaling at large scales from the original anticorrelated to uncorrelated behavior. In contrast, positively correlated signals show no observable changes in the local scaling for up to 65% of data loss, while for larger percentage of data loss, the local scaling shows overestimated regions (with higher local exponent) at small scales, followed by underestimated regions (with lower local exponent) at large scales. Finally, we investigate how the scaling is affected by the average length, probability distribution, and percentage of the remaining data segments in comparison to the removed segments. We find that the average length mu_{r} of the remaining segments is the key parameter which determines the scales at which the local scaling exponent has a maximum deviation from its original value. Interestingly, the scales where the maximum deviation occurs follow a power-law relationship with mu_{r} . Whereas the percentage of data loss determines the extent of the deviation. The results presented in this paper are useful to correctly interpret the scaling properties obtained from signals with extreme data loss.

Levels of complexity in scale-invariant neural signals.

Many physical and physiological signals exhibit complex scale-invariant features characterized by 1/f scaling and long-range power-law correlations, indicating a possibly common control mechanism. Specifically, it has been suggested that dynamical processes, influenced by inputs and feedback on multiple time scales, may be sufficient to give rise to 1/f scaling and scale invariance. Two examples of physiologic signals that are the output of hierarchical multiscale physiologic systems under neural control are the human heartbeat and human gait. Here we show that while both cardiac interbeat interval and gait interstride interval time series under healthy conditions have comparable 1/f scaling, they still may belong to different complexity classes. Our analysis of the multifractal scaling exponents of the fluctuations in these two signals demonstrates that in contrast to the multifractal behavior found in healthy heartbeat dynamics, gait time series exhibit less complex, close to monofractal behavior. Further, we find strong anticorrelations in the sign and close to random behavior for the magnitude of gait fluctuations at short and intermediate time scales, in contrast to weak anticorrelations in the sign and strong positive correlation for the magnitude of heartbeat interval fluctuations-suggesting that the neural mechanisms of cardiac and gait control exhibit different linear and nonlinear features. These findings are of interest because they underscore the limitations of traditional two-point correlation methods in fully characterizing physiological and physical dynamics. In addition, these results suggest that different mechanisms of control may be responsible for varying levels of complexity observed in physiological systems under neural regulation and in physical systems that possess similar 1/f scaling.

Photochromism of diarylethene single molecules in polymer matrices.

Robust fluorescent photoswitching molecules, having perylene bisimide as the fluorescent unit and diarylethene as the switching unit, were prepared, and their photochromic reactions were measured at the single-molecule level in various polymer matrices. The histograms of the fluorescent on and off times were found to deviate from normal exponential distribution and showed a peak when the molecules are embedded in rigid polymer matrices, such as Zeonex or poly(methyl methacrylate) (PMMA). In soft polymer matrices, such as poly(n-buthyl methacrylate) (PnBMA), exponential distribution was observed for the on and off times. The abnormal distribution suggests that the quantum yields of the photoreactions are not constant and the molecules undergo the reactions after absorbing a certain number of photons. A multilocal minima model was proposed to explain the environmental effect.

Possible involvement of ATP-purinoceptor signalling in the intercellular synchronization of intracellular Ca2+ oscillation in cultured cardiac myocytes.

Isolated and cultured neonatal cardiac myocytes contract spontaneously and cyclically. The contraction rhythms of two isolated cardiac myocytes, each of which beats at different frequencies at first, become synchronized after the establishment of mutual contacts, suggesting that mutual entrainment occurs due to electrical and/or mechanical interactions between two myocytes. The intracellular concentration of free Ca(2+) also changes rhythmically in association with the rhythmic contraction of myocytes (Ca(2+) oscillation), and such a Ca(2+) oscillation was also synchronized among cultured cardiac myocytes. In this study, we investigated whether intercellular communication other than via gap junctions was involved in the intercellular synchronization of intracellular Ca(2+) oscillation in spontaneously beating cultured cardiac myocytes. Treatment with either blockers of gap junction channels or an un-coupler of E-C coupling did not affect the intercellular synchronization of Ca(2+) oscillation. In contrast, treatment with a blocker of P2 purinoceptors resulted in the asynchronization of Ca(2+) oscillatory rhythms among cardiac myocytes. The present study suggested that the extracellular ATP-purinoceptor system was responsible for the intercellular synchronization of Ca(2+) oscillation among cardiac myocytes.

Coupled oscillator systems of cultured cardiac myocytes: fluctuation and scaling properties.

Isolated and cultured neonatal cardiac myocytes exhibit autonomous rhythmic contraction, and their dynamics vary dramatically depending on the extent of mutual coupling among individual myocytes. We study the temporal changes of interbeat interval series in aggregated systems of spontaneously beating cultured neonatal rat cardiac myocytes and observe a rich variety of complex, nonlinear features such as frequent alternations, bistability, and periodic spikes. Fluctuation analysis of the interval series reveals that there occurs a transition in scaling behavior from persistent correlations to antipersistent correlations as the coupling develops with incubation time. Additionally, we perform computer simulations using interacting Bonhoeffer-van der Pol oscillators to understand the effects of coupling on the fluctuation dynamics of each constituent oscillator. We find that the formation of strong and heterogeneous coupling among the oscillators is a key factor to yield the complexity in the interval series as well as in the scaling behavior.