Impact of series length on statistical precision and sensitivity of autocorrelation assessment in human locomotion

Comparison of the effects of rhythmic vibrotactile stimulations and rhythmic auditory stimulations on Parkinson's disease patients' gait variability: a pilot study

INTRODUCTION

Parkinson's disease patients' gait is characterized by shorter step length, reduced gait velocity and deterioration of temporal organization of stride duration variability (modified Long Range Autocorrelations). The objective of this study was to compare effects of rhythmic auditory stimulations (RAS) and Rhythmic Vibrotactile Stimulations (RVS) on Parkinson's disease patients' gait.

METHODS

Ten Parkinson's disease patients performed three walking conditions lasting 5-7 min each: control condition (CC), RAS condition and RVS condition. Inertial measurement units were used to assess spatiotemporal gait parameters. Stride duration variability was assessed in terms of magnitude using coefficient of variation and in terms of temporal organization (i.e., Long Range Autocorrelations computation) using the evenly spaced averaged Detrended Fluctuation Analysis (α-DFA exponent).

RESULTS

Gait velocity was significantly higher during RAS condition than during CC (Cohen's d = 0.52) and similar to RVS condition (Cohen's d = 0.17). Cadence was significantly higher during RAS (Cohen's d = 0.77) and RVS (Cohens' d = 0.56) conditions than during CC. Concerning variability, no difference was found either for mean coefficient of variation or mean α-DFA between conditions. However, a great variability of individual results between the RAS and the RVS conditions is to be noted concerning α-DFA.

CONCLUSIONS

RAS and RVS improved similarly PD patients' spatiotemporal gait parameters, without modifying stride duration variability in terms of magnitude and temporal organization at group level. Future studies should evaluate the relevant parameters for administering the right cueing type for the right patient.

TRIAL REGISTRATION

ClinicalTrial.gov registration number NCT05790759, date of registration: 16/03/2023, retrospectively registered.

Effects of overground gait training assisted by a wearable exoskeleton in patients with Parkinson's disease

BACKGROUND

In the recent past, wearable devices have been used for gait rehabilitation in patients with Parkinson's disease. The objective of this paper is to analyze the outcome of a wearable hip orthosis whose assistance adapts in real time to the patient's gait kinematics via adaptive oscillators. In particular, this study focuses on a metric characterizing natural gait variability, i.e., the level of long-range autocorrelations (LRA) in series of stride durations.

METHODS

Eight patients with Parkinson's disease (Hoehn and Yahr stages 1[Formula: see text]2.5) performed overground gait training three times per week for four consecutive weeks, assisted by a wearable hip orthosis. Gait was assessed based on performance metrics such as the hip range of motion, speed, stride length and duration, and the level of LRA in inter-stride time series assessed using the Adaptive Fractal Analysis. These metrics were measured before, directly after, and 1 month after training.

RESULTS

After training, patients increased their hip range of motion, their gait speed and stride length, and decreased their stride duration. These improvements were maintained 1 month after training. Regarding long-range autocorrelations, the population's behavior was standardized towards a metric closer to the one of healthy individuals after training, but with no retention after 1 month.

CONCLUSION

This study showed that an overground gait training with adaptive robotic assistance has the potential to improve key gait metrics that are typically affected by Parkinson's disease and that lead to higher prevalence of fall.

TRIAL REGISTRATION

ClinicalTrials.gov Identifer NCT04314973. Registered on 11 April 2020.

Adaptive walking assistance does not impact long-range stride-to-stride autocorrelations in healthy people

Many studies have demonstrated in the past that the level of long-range autocorrelations in series of stride durations, characterizing natural gait variability, is impacted by external constraints, such as treadmill or metronome, or by pathologies, such as Parkinson's or Huntington's disease. Nevertheless, no one has analyzed the effects on this metric of a gait constrained by a robot-mediated walking assistance, which intrinsically tends to normalize the gait pattern. This paper focuses on the influence of a wearable active pelvis orthosis on the level of long-range autocorrelations in series of stride durations. Ten healthy participants, aged between 55 and 77 yr, performed four overground walking sessions, wearing this orthosis, and with different assistive parameters. This study showed that the adaptive assistance provided by this device has the potential of improving gait metrics that are typically affected by aging, such as the hip range of motion, walking speed, stride length, and stride duration, without impacting natural gait variability, i.e., the level of long-range autocorrelations in series of stride durations. This combination is virtuous toward the design of an assistive device for people with locomotion disorders resulting in deteriorated levels of long-range autocorrelations, such as patients with Parkinson's disease.NEW & NOTEWORTHY This study is the first that investigates the effects of a wearable active pelvis orthosis using an oscillator-based adaptive assistance on the level of long-range autocorrelations in series of stride durations during overground walking. It is also the first to compare the effects of different assistance settings on spatiotemporal gait metrics.

Stride segmentation of inertial sensor data using statistical methods for different walking activities

Human gait data can be collected using inertial measurement units (IMUs). An IMU is an electronic device that uses an accelerometer and gyroscope to capture three-axial linear acceleration and three-axial angular velocity. The data so collected are time series in nature. The major challenge associated with these data is the segmentation of signal samples into stride-specific information, that is, individual gait cycles. One empirical approach for stride segmentation is based on timestamps. However, timestamping is a manual technique, and it requires a timing device and a fixed laboratory set-up which usually restricts its applicability outside of the laboratory. In this study, we have proposed an automatic technique for stride segmentation of accelerometry data for three different walking activities. The autocorrelation function (ACF) is utilized for the identification of stride boundaries. Identification and extraction of stride-specific data are done by devising a concept of tuning parameter ( $t_{p}$ ) which is based on minimum standard deviation ( $\sigma$ ). Rigorous experimentation is done on human activities and postural transition and Osaka University – Institute of Scientific and Industrial Research gait inertial sensor datasets. Obtained mean stride duration for level walking, walking upstairs, and walking downstairs is 1.1, 1.19, and 1.02 s with 95% confidence interval [1.08, 1.12], [1.15, 1.22], and [0.97, 1.07], respectively, which is on par with standard findings reported in the literature. Limitations of accelerometry and ACF are also discussed. stride segmentation; human activity recognition; accelerometry; gait parameter estimation; gait cycle; inertial measurement unit; autocorrelation function; wearable sensors; IoT; edge computing; tinyML.

Motor Synergies Measurement Reveals the Relevant Role of Variability in Reward-Based Learning

Currently, it is not fully understood how motor variability is regulated to ease of motor learning processes during reward-based tasks. This study aimed to assess the potential relationship between different dimensions of motor variability (i.e., the motor variability structure and the motor synergies variability) and the learning rate in a reward-based task developed using a two-axis force sensor in a computer environment. Forty-four participants performed a pretest, a training period, a posttest, and three retests. They had to release a virtual ball to hit a target using a vertical handle attached to a dynamometer in a computer-simulated reward-based task. The participants' throwing performance, learning ratio, force applied, variability structure (detrended fluctuation analysis, DFA), and motor synergy variability (good and bad variability ratio, GV/BV) were calculated. Participants with higher initial GV/BV displayed greater performance improvements than those with lower GV/BV. DFA did not show any relationship with the learning ratio. These results suggest that exploring a broader range of successful motor synergy combinations to achieve the task goal can facilitate further learning during reward-based tasks. The evolution of the motor variability synergies as an index of the individuals' learning stages seems to be supported by our study.

Influence of Autocorrelated Rhythmic Auditory Stimulations on Parkinson's Disease Gait Variability: Comparison With Other Auditory Rhythm Variabilities and Perspectives

Parkinson's Disease patients suffer from gait impairments such as reduced gait speed, shortened step length, and deterioration of the temporal organization of stride duration variability (i.e., breakdown in Long-Range Autocorrelations). The aim of this study was to compare the effects on Parkinson's Disease patients' gait of three Rhythmic Auditory Stimulations (RAS), each structured with a different rhythm variability (isochronous, random, and autocorrelated). Nine Parkinson's Disease patients performed four walking conditions of 10-15 min each: Control Condition (CC), Isochronous RAS (IRAS), Random RAS (RRAS), and Autocorrelated RAS (ARAS). Accelerometers were used to assess gait speed, cadence, step length, temporal organization (i.e., Long-Range Autocorrelations computation), and magnitude (i.e., coefficient of variation) of stride duration variability on 512 gait cycles. Long-Range Autocorrelations were assessed using the evenly spaced averaged Detrended Fluctuation Analysis (α-DFA exponent). Spatiotemporal gait parameters and coefficient of variation were not modified by the RAS. Long-Range Autocorrelations were present in all patients during CC and ARAS although all RAS conditions altered them. The α-DFA exponents were significantly lower during IRAS and RRAS than during CC, exhibiting anti-correlations during IRAS in seven patients. α-DFA during ARAS was the closest to the α-DFA during CC and within normative data of healthy subjects. In conclusion, Isochronous RAS modify patients' Long-Range Autocorrelations and the use of Autocorrelated RAS allows to maintain an acceptable level of Long-Range Autocorrelations for Parkinson's Disease patients' gait.

Test-Retest Reliability and the Effects of Walking Speed on Stride Time Variability During Continuous, Overground Walking in Healthy Young Adults

Studies have investigated the reliability and effect of walking speed on stride time variability during walking trials performed on a treadmill. The objective of this study was to investigate the reliability of stride time variability and the effect of walking speed on stride time variability, during continuous, overground walking in healthy young adults. Participants completed: (1) 2 walking trials at their preferred walking speed on 1 day and another trial 2 to 4 days later and (2) 1 trial at their preferred walking speed, 1 trial approximately 20% to 25% faster than their preferred walking speed, and 1 trial approximately 20% to 25% slower than their preferred walking speed on a separate day. Data from a waist-mounted accelerometer were used to determine the consecutive stride times for each trial. The reliability of stride time variability outcomes was generally poor (intraclass correlations: .167-.487). Although some significant differences in stride time variability were found between the preferred walking speed, fast, and slow trials, individual between-trial differences were generally below the estimated minimum difference considered to be a real difference. The development of a protocol to improve the reliability of stride time variability outcomes during continuous, overground walking would be beneficial to improve their application in research and clinical settings.

Benefits of nonlinear analysis indices of walking stride interval in the evaluation of neurodegenerative diseases

Indices characterising the long-range temporal structure of walking stride interval (SI) variability such as Hurst exponent (H) and fractal dimension (D) may be used in addition to indices measuring the amount of variability like the coefficient of variation (CV). We assess the added value of the former indices in a clinical neurological context. Our aim is to demonstrate that they provide a clinical significance in aging and in frequent neurodegenerative diseases such as Parkinson's disease, Huntington, and amyotrophic lateral sclerosis. Indices assessing the temporal structure of variability are mainly dependent on SI time series length and algorithms used, making quantitative comparisons between different studies difficult or even impossible. Here, we recompute these indices from available SI time series, either from our lab or from online databases. More precisely, we recompute CV, H, and D in a unified way. The average SI is also added to the measured parameters. We confirm that variability indices are relevant indicators of aging process and neurodegenerative diseases. While CV is sensitive to aging process and pathology, it does not discriminate between specific neurodegenerative diseases. H, which measures predictability of SI, significantly decreases with age but increases in patients suffering from amyotrophic lateral sclerosis. D, catching complexity of SI, is correlated with total functional capacity in patients with Huntington's disease. We conclude that the computation of H complements the clinical diagnosis of walking in patients with neurodegenerative diseases and we recommend it as a relevant supplement to classical CV or averaged SI. Since H and D indices did not lead to the same observations, suggesting the multi-fractal nature of SI dynamics, we recommend to open clinical gait analysis to the evaluation of more parameters.

Immersive Virtual Reality to Restore Natural Long-Range Autocorrelations in Parkinson's Disease Patients' Gait During Treadmill Walking

Effects of treadmill walking on Parkinson’s disease (PD) patients’ spatiotemporal gait parameters and stride duration variability, in terms of magnitude [coefficient of variation (CV)] and temporal organization [long range autocorrelations (LRA)], are known. Conversely, effects on PD gait of adding an optic flow during treadmill walking using a virtual reality headset, to get closer to an ecological walk, is unknown. This pilot study aimed to compare PD gait during three conditions: Overground Walking (OW), Treadmill Walking (TW), and immersive Virtual Reality on Treadmill Walking (iVRTW). Ten PD patients completed the three conditions at a comfortable speed. iVRTW consisted in walking at the same speed as TW while wearing a virtual reality headset reproducing an optic flow. Gait parameters assessed were: speed, step length, cadence, magnitude (CV) and temporal organization (evenly spaced averaged Detrended Fluctuation Analysis, α exponent) of stride duration variability. Motion sickness was assessed after TW and iVRTW using the Simulator Sickness Questionnaire (SSQ). Step length was greater (p = 0.008) and cadence lower (p = 0.009) during iVRTW compared to TW while CV was similar (p = 0.177). α exponent was similar during OW (0.77 ± 0.07) and iVRTW (0.76 ± 0.09) (p = 0.553). During TW, α exponent (0.85 ± 0.07) was higher than during OW (p = 0.039) and iVRTW (p = 0.016). SSQ was similar between TW and iVRTW (p = 0.809). iVRTW is tolerable, could optimize TW effects on spatiotemporal parameters while not increasing CV in PD. Furthermore, iVRTW could help to capture the natural LRA of PD gait in laboratory settings and could potentially be a challenging second step in PD gait rehabilitation.

Fractal auditory stimulation has greater benefit for people with Parkinson's disease showing more random gait pattern

BACKGROUND

Healthy gait dynamics are characterized by the presence of fractal, persistent stride-to-stride variations, which become more random with Parkinson's disease (PD). Rhythmic auditory stimulation with fractal beat-to-beat variations can change gait dynamics in people with PD toward more persistence.

RESEARCH QUESTION

How does gait in people with PD change when synchronizing steps with fractal melodic metronomes with different step-to-beat ratios, and which stimulus do they prefer?

METHODS

In this cross-sectional study, 15 people with PD and 15 healthy older adults walked over-ground in three conditions: self-paced, paced by a fractal auditory stimulus with a 1:1 step-to-beat ratio ('metronome'), and fractal auditory stimulus with a 1:2 step-to-beat ratio ('music'). Gait dynamics were recorded with instrumented insoles, and detrended fluctuation analysis (DFA) was applied to the series of stride time intervals. Stimuli preference was assessed using Likert-like scales and open-ended questions. ANOVAs were used to compare mean, coefficient of variation, α-DFA, and the responses from the continuous Likert scales. Pearson correlations were used to assess the relationship between 'music' and 'metronome' enjoyment or difficulty with gait outcomes, and to determine the association between baseline α-DFA and changes due to the stimuli.

RESULTS

Our major findings are that (i) stride-to-stride variations were more persistent with the 'metronome' compared to baseline for both groups, (ii) the effect was greater for people with lower α-DFA at baseline (i.e., more random stride-to-stride variations), and (iii) both groups found the 'metronome' less difficult to synchronize with.

SIGNIFICANCE

This study showed that people with PD and healthy older adults walk with higher statistical persistence in their stride-to-stride variations when instructed to synchronize their steps with a fractal stimulus. Participants with lower persistence at baseline benefited the most from the fractal 'metronome', highlighting the importance to develop patient-centered tests and interventions.

Digital natives and dual task: Handling it but not immune against cognitive-locomotor interferences

Digital natives developed in an electronic dual tasking world. This paper addresses two questions. Do digital natives respond differently under a cognitive load realized during a locomotor task in a dual-tasking paradigm and how does this address the concept of safety? We investigate the interplay between cognitive (talking and solving Raven’s matrices) and locomotor (walking on a treadmill) tasks in a sample of 17 graduate level participants. The costs of dual-tasking on gait were assessed by studying changes in stride interval time and its variability at long-range. A safety index was designed and computed from total relative change between the variability indices in the single walking and dual-task conditions. As expected, results indicate high Raven’s scores with gait changes found between the dual task conditions compared to the single walking task. Greater changes are observed in the talking condition compared to solving Raven’s matrices, resulting in high safety index values observed in 5 participants. We conclude that, although digital natives are efficient in performing the dual tasks when they are not emotional-based, modification of gait are observable. Due to the variation within participants and the observation of high safety index values in several of them, individuals that responded poorly to low cognitive loads should be encouraged to not perform dual task when executing a primate task of safety to themselves or others.

Fractal Analysis of Human Gait Variability via Stride Interval Time Series

Fractal analysis of stride interval time series is a useful tool in human gait research which could be used as a marker for gait adaptability, gait disorder, and fall risk among patients with movement disorders. This study is designed to systematically and comprehensively investigate two practical aspects of fractal analysis which significantly affect the outcome: the series length and the parameters used in the algorithm. The Hurst exponent, scaling exponent, and/or fractal dimension are computed from both simulated and experimental data using three fractal methods, namely detrended fluctuation analysis, box-counting dimension, and Higuchi's fractal dimension. The advantages and drawbacks of each method are discussed, in terms of biases and variability. The results demonstrate that a careful selection of fractal analysis methods and their parameters is required, which is dependent on the aim of study (either analyzing differences between experimental groups or estimating an accurate determination of fractal features). A set of guidelines for the selection of the fractal methods and the length of stride interval time series is provided, along with the optimal parameters for a robust implementation for each method.

Effect of sampling frequency on fractal fluctuations during treadmill walking

The temporal dynamics of stride-to-stride fluctuations in steady-state walking reveal important information about locomotor control and can be quantified using so-called fractal analyses, notably the detrended fluctuation analysis (DFA). Gait dynamics are often collected during treadmill walking using 3-D motion capture to identify gait events from kinematic data. The sampling frequency of motion capture systems may impact the precision of event detection and consequently impact the quantification of stride-to-stride variability. This study aimed i) to determine if collecting multiple walking trials with different sampling frequency affects DFA values of spatiotemporal parameters during treadmill walking, and ii) to determine the reliability of DFA values across downsampled conditions. Seventeen healthy young adults walked on a treadmill while their gait dynamics was captured using different sampling frequency (60, 120 and 240 Hz) in each condition. We also compared data from the highest sampling frequency to downsampled versions of itself. We applied DFA to the following time series: step length, time and speed, and stride length, time and speed. Reliability between experimental conditions and between downsampled conditions were measured with 1) intraclass correlation estimates and their 95% confident intervals, calculated based on a single-measurement, absolute-agreement, two-way mixed-effects model (ICC 3,1), and 2) Bland-Altman bias and limits of agreement. Both analyses revealed a poor reliability of DFA results between conditions using different sampling frequencies, but a relatively good reliability between original and downsampled spatiotemporal variables. Collectively, our results suggest that using sampling frequencies of 120 Hz or 240 Hz provide similar results, but that using 60 Hz may alter DFA values. We recommend that gait kinematics should be collected at around 120 Hz, which provides a compromise between event detection accuracy and processing time.

Fractal analysis of gait in people with Parkinson's disease: three minutes is not enough

BACKGROUND

The fractal dynamics of gait variability in people with Parkinson's disease has been studied by applying the detrended fluctuation analysis (DFA) to short time series (<200 strides). However, DFA is sensitive to time series length, and it is unclear if DFA results from short time series are reliable and if they reflect the fractal dynamics of longer time series.

RESEARCH QUESTION

Is DFA reliable when applied to short time series?

METHODS

We applied DFA to stride time series from five 3-min trials and one 15-min trial in 12 people with Parkinson's disease, 14 healthy older adults and 14 healthy young adults walking overground. Within each group, intraclass correlations (ICC 3,1) were performed to assess the reliability of i) the five 3-min trials together, ii) each 3-min trials to the 15-min trial, and iii) the first 150 strides from the 15-min trial to the full 15-min trial.

RESULTS

Our three main findings are that 1) stride time α-DFA values are not consistent from trial-to-trial for short stride time series, 2) stride time α-DFA values from each 3-min trials are not consistent when compared to stride time α-DFA values from a 15-min trial, and 3) stride time α-DFA values from the first 150 strides of the 15-min trial are not consistent when compared to α-DFA values from the full 15-min trial.

SIGNIFICANCE

Our results confirm that α-DFA values from 3-min walking trials are not reliable, and that they do not reflect the scale invariant properties of longer time series. This suggests that previous studies assessing the fractal dynamics of gait variability from about 3-min walking must be interpreted with caution. A major clinical implication is that DFA cannot be used to study gait in people unable to perform 500 strides continuously.