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Introduction:Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods:The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion:Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02m, walking speed ≤0.02m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06m, walking speed 0.03–0.05m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (SDA, including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-hours of real-world unsupervised activity. Excellent absolute agreement (ICC > 0.95) and very limited mean absolute errors were observed for all cohorts and DMOs (cadence ≤ 0.61 steps/min, stride length ≤ 0.02 m, walking speed ≤ 0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the SDA (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-hours acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

Background: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a multi-task and multi-phase protocol for simulating real-world gait accounting for all these factors within a single set of observations carried out within a limited laboratory space, while ensuring minimisation of participant burden and safety.Methods: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants’ strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohort groups that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson’s Disease (PD), Multiple Sclerosis (MS), Proximal Femoral Fracture (PFF), Chronic Obstructive Pulmonary Disease (COPD) or Congestive Heart Failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks.Results: The protocol proved to be safe and feasible and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a relatively realistic representation of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life.Conclusions: The suitability of the protocol for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy.Trial registration: ISRCTN – 12246987.

Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis(OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-meter walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique (“super learning”) to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain.Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR=0.80 (0.64-1.00) and aOR=0.79(0.66-0.95), respectively].

The “levodopa-overdose hypothesis” posits that dopaminergic replacement therapy (1) increases performance on tasks that depend on the nigrostriatal-pathway (e.g., motor-control circuits), yet (2) decreases performance on tasks that depend upon the mesocorticolimbic-pathway (e.g., prefrontal cortex, PFC). Previous work in Parkinson’s disease (PD) investigated this model while focusing on cognitive function. Here, we evaluated whether this model applies to gait in patients with PD and freezing of gait (FOG). Forty participants were examined in both the OFF anti-Parkinsonian medication state (hypo-dopaminergic) and ON state (hyper-dopaminergic) while walking with and without the concurrent performance of a serial subtraction task. Wireless functional near-infrared spectroscopy measured PFC activation during walking. Consistent with the “overdose-hypothesis”, performance on the subtraction task decreased (p = 0.027) after dopamine intake. Moreover, the effect of walking condition on PFC activation depended on the dopaminergic state (i.e., interaction effect p = 0.001). Gait significantly improved after levodopa administration (p

Background: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. Methods: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. Results: The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. Conclusions: The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. Trial registration: ISRCTN-12246987. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 820820. This JU receives support from the European Union’s Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). This study was also supported by the National Institute for Health Research (NIHR) through the Sheffield Biomedical Research Centre (BRC, Grant Number IS-BRC-1215–20017). AY, LA, LR and SDD are also supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Center (BRC) based at Newcastle Upon Tyne Hospital NHS Foundation Trust and Newcastle University. AY, LA, LR and SDD are also supported by the NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle upon Tyne Hospitals NHS Foundation Trust. ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019–2023” Program (CEX2018-000806-S), and from the Generalitat de Catalunya through the CERCA Program. All opinions are those of the authors and not the funders. Neither IMI nor the European Union, EFPIA, NHS, NIHR, DHSC or any Associated Partners are responsible for any use that may be made of the information contained herein.

In this study, we present algorithms developed for gait analysis, but suitable for many other signal processing tasks. A novel general-purpose algorithm for extremum estimation of quasi-periodic noisy signals is proposed. This algorithm is both flexible and robust, and allows custom adjustments to detect a predetermined wave pattern while being immune to signal noise and variability. A method for signal segmentation was also developed for analyzing kinematic data recorded while performing on obstacle avoidance task. The segmentation allows detecting preparation and recovery phases related to obstacle avoidance. A simple kernel-based clustering method was used for classification of unsupervised data containing features of steps within the walking trial and discriminating abnormal from regular steps. Moreover, a novel algorithm for missing data approximation and adaptive signal filtering is also presented. This algorithm allows restoring faulty data with high accuracy based on the surrounding information. In addition, a predictive machine learning technique is proposed for supervised multiclass labeling with non-standard label structure.

The potential of using wearable technologies for the objective assessment of motor symptoms in Parkinson's disease (PD) has gained prominence recently. Nonetheless, compared to tremor and gait impairment, less emphasis has been placed on the quantification of bradykinesia and rigidity. This review aimed to consolidate the existing research on objective measurement of bradykinesia and rigidity in PD through the use of wearables, focusing on the continuous monitoring of these two symptoms in free-living environments. A search of PubMed was conducted through a combination of keyword and MeSH searches. We also searched the IEEE, Google Scholar, Embase, and Scopus databases to ensure thorough results and to minimize the chances of missing relevant studies. Papers published after the year 2000 with sample sizes greater than five were included. Studies were assessed for quality and information was extracted regarding the devices used and their location on the body, the setting and duration of the study, the "gold standard" used as a reference for validation, the metrics used, and the results of each paper. Thirty-one and eight studies met the search criteria and evaluated bradykinesia and rigidity, respectively. Several studies reported strong associations between wearable-based measures and the gold-standard references for bradykinesia, and, to a lesser extent, rigidity. Only a few, pilot studies investigated the measurement of bradykinesia and rigidity in the home and free-living settings. While the current results are promising for the future of wearables, additional work is needed on their validation and adaptation in ecological, free-living settings. Doing so has the potential to improve the assessment and treatment of motor fluctuations and symptoms of PD more generally through real-time objective monitoring of bradykinesia and rigidity.

Introduction: Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device. Methods and analysis: This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment. Ethics and dissemination: The study has been granted ethics approval by the centre's committees (London-Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available. Trial registration number: ISRCTN (12246987). We acknowledge support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. SDD, AY and LRo are also supported by the Newcastle Biomedical Research Centre (BRC) based at Newcastle upon Tyne and Newcastle University. CM, BS, LVG and EB are also supported by the Sheffield Biomedical Research Centre (BRC) based at the Sheffield Teaching Hospital and the University of Sheffield. The work was also supported by the NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle upon Tyne Hospitals NHS Foundation Trust and the CRF at the Sheffield Teaching Hospital. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care or the funders.This study was co-funded by the European Union’s Horizon 2020 research and innovation programme and EFPIA via the Innovative Medicine Initiative 2 (Mobilise-D project, grant number IMI22017-13-7-820820). The views expressed are those of the authors and not necessarily those of the IMI, the European Union, the EFPIA, or any Associated Partners. We acknowledge the support of Grünenthal GmbH via the funding of a PhD scholarship directly dedicated to the technical validation protocol.

Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson's disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the &ldquo, ground-truth&rdquo, for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.

Background Wearable sensors are increasingly employed to quantify diverse aspects of mobility. We developed novel tandem walking (TW) metrics, validated these measures using data from community-dwelling older adults, and evaluated their association with mobility disability and measures of gait and postural control. Methods Six hundred ninety-three community-dwelling older adults (age: 78.69 ± 7.12 years) wore a 3D accelerometer on their lower back while performing 3 tasks: TW, usual-walking, and quiet standing. Six new measures of TW were extracted from the sensor data along with the clinician’s conventional assessment of TW missteps (ie, trip other loss of balance in which recovery occurred to prevent a fall) and duration. Principal component analysis transformed the 6 new TW measures into 2 summary TW composite factors. Logistic regression models evaluated whether these TW factors were independently associated with mobility disability. Results Both TW factors were moderately related to the TW conventional measures (r < 0.454, p < .001) and were mildly correlated with usual-walking (r < 0.195, p < .001) and standing, postural control (r < 0.119, p < .001). The TW frequency composite factor (p = .008), but not TW complexity composite factor (p = .246), was independently associated with mobility disability in a model controlling for age, sex, body mass index, race, conventional measures of TW, and other measures of gait and postural control. Conclusions Sensor-derived TW metrics expand the characterization of gait and postural control and suggest that they reflect a relatively independent domain of mobility. Further work is needed to determine if these metrics improve risk stratification for other adverse outcomes (eg, falls and incident disability) in older adults.

The "levodopa-overdose hypothesis" posits that dopaminergic replacement therapy (1) increases performance on tasks that depend on the nigrostriatal-pathway (e.g., motor-control circuits), yet (2) decreases performance on tasks that depend upon the mesocorticolimbic-pathway (e.g., prefrontal cortex, PFC). Previous work in Parkinson's disease (PD) investigated this model while focusing on cognitive function. Here, we evaluated whether this model applies to gait in patients with PD and freezing of gait (FOG). Forty participants were examined in both the OFF anti-Parkinsonian medication state (hypo-dopaminergic) and ON state (hyper-dopaminergic) while walking with and without the concurrent performance of a serial subtraction task. Wireless functional near-infrared spectroscopy measured PFC activation during walking. Consistent with the "overdose-hypothesis", performance on the subtraction task decreased (p = 0.027) after dopamine intake. Moreover, the effect of walking condition on PFC activation depended on the dopaminergic state (i.e., interaction effect p = 0.001). Gait significantly improved after levodopa administration (p 0.001). Nonetheless, PFC activation was higher (p = 0.013) in this state than in the OFF state during usual-walking. This increase in PFC activation in the ON state suggests that dopamine treatment interfered with PFC functioning. Otherwise, PFC activation, putatively a reflection of cognitive compensation, should have decreased. Moreover, in contrast to the OFF state, in the ON state, PFC activation failed to increase (p = 0.313) during dual-tasking, perhaps due to a "ceiling effect". These findings extend the "levodopa-overdose hypothesis" and suggest that it also applies to gait in PD patients. While dopaminergic therapy improves certain aspects of motor performance, optimal treatment should consider the "double-edged sword" of levodopa.

Background Among patients with Parkinson’s disease (PD), gait is typically disturbed and less automatic. These gait changes are associated with impaired rhythmicity and increased prefrontal activation, presumably in an attempt to compensate for reduced automaticity. Research question We investigated whether during treadmill walking, when the pace is determined and fixed, prefrontal activation in patients with PD is lower, as compared to over-ground walking. Methods Twenty patients with PD (age: 69.8 ± 6.5 yrs.; MoCA: 26.9 ± 2.4; disease duration: 7.9 ± 4.2 yrs) walked at a self-selected walking speed over-ground and on a treadmill. A wireless functional near infrared spectroscopy (fNIRS) system measured prefrontal lobe activation, i.e., oxygenated hemoglobin (Hb02) in the pre-frontal area. Gait was evaluated using 3D-accelerometers attached to the lower back and ankles (Opal™, APDM). Dynamic gait stability was assessed using the maximum Lyapunov exponent to investigate automaticity of the walking pattern. Results Hb02 was lower during treadmill walking than during over-ground walking (p = 0.001). Gait stability was greater on the treadmill, compared to over-ground walking, in both the anteroposterior and medio-lateral axes (p Significance These findings support the notion that when gait is externally paced, prefrontal lobe activation is reduced in patients with PD, perhaps reflecting a reduced need for compensatory cognitive mechanisms.

Freezing of gait (FoG) is a motor impairment among patients with advanced Parkinson’s disease, associated with falls and negative impact on patient’s quality of life. Detecting such freezes allows real-time gait monitoring to reduce the risk of falls. We investigate the correlation between wrist movements and the freezing of the gait in Parkinson’s disease, targeting FoG-detection from wrist-worn sensing data. While most of research focuses on placing inertial sensors on lower limb, i.e., foot, ankle, thigh, we focus on the wrist as an alternative placement. Commonly worn accessories at the wrist such as watches or wristbands are more likely to be accepted and easier to be worn by elderly users, especially subjects with motor problems. Experiments on data from 11 subjects with Parkinson’s disease and FoG show there are specific features from wrist movements which are related to gait freeze, such the power on different frequency ranges and statistical information from acceleration and rotation data. Moreover, FoG can be detected by using wrist motion and machine learning models with a FoG hit rate of 0.9, and a specificity between 0.66 and 0.8. Compared with the state-of-the-art lower limb information used to detect FoG, the wrist increases the number of false detected events, while preserving the FoG hit-rate and detection latency. This suggests that wrist sensors can be a feasible alternative to the cumbersome placement on the legs.

Background Walking is a volitional behavior that requires planning and initiation before a step is observed. Following a signal to begin walking, studies of gait initiation in specialized labs have identified three phases that occur during the transition from a standing position via anticipatory postural adjustment (APA) to the first step. Routine instrumented gait testing outside of the laboratory setting focuses on gait execution and does not include gait initiation measures. Research question Can a single IMU sensor be used for performing gait initiation evaluations outside the lab? Methods We recorded walking in young (N = 41) and older (N = 26) adults using an instrumented gait mat while they were wearing a 3D accelerometer on their lower back. Subjects were instructed to begin walking following an auditory signal. An algorithm was developed to extract the following measures from the acceleration signal: gait initiation time, measured from the start of the auditory cue to begin walking and ends at the heel-strike of the swing leg, time-to-APA (reaction time), APA duration and swing time (execution of the first step). Results Intraclass correlation coefficient analysis showed good to excellent agreement between gait initiation metrics obtained with the gait mat and the wearable sensor (mean 0.88, range [0.75-0.96]). Except for swing time, all measures were longer in the older subjects, compared to the young adults (p Significance Extracting gait initiation measures from routine instrumented gait testing may facilitate studies that can better determine the extent to which impaired gait planning and execution contribute to mobility impairments.

Prolonged walking is typically impaired among people with multiple sclerosis (pwMS), however, it is unclear what the contributing factors are or how to evaluate this deterioration. We aimed to determine which gait features become worse during sustained walking and to examine the clinical correlates of gait fatigability in pwMS. Fifty-eight pwMS performed the 6-min walk test while wearing body-fixed sensors. Multiple gait domains (e.g., pace, rhythm, variability, asymmetry and complexity) were compared across each minute of the test and between mild- and moderate-disability patient groups. Associations between the decline in gait performance (i.e., gait fatigability) and patient-reported gait disability, fatigue and falls were also determined. Cadence, stride time variability, stride regularity, step regularity and gait complexity significantly deteriorated during the test. In contrast, somewhat surprisingly, gait speed and swing time asymmetry did not change. As expected, subjects with moderate disability (n = 24) walked more poorly in most gait domains compared to the mild-disability group (n = 34). Interestingly, a group × fatigue interaction effect was observed for cadence and gait complexity; these measures decreased over time in the moderate-disability group, but not in the mild group. Gait fatigability rate was significantly correlated with physical fatigue, gait disability, and fall history. These findings suggest that sustained walking affects specific aspects of gait, which can be used as markers for fatigability in MS. This effect on gait depends on the degree of disability, and may increase fall risk in pwMS. To more fully understand and monitor correlates that reflect everyday walking in pwMS, multiple domains of gait should be quantified.

Background The electroencephalogram (EEG) can be a useful tool to investigate the neurophysiology of gait during walking. Our aims were to develop an approach that identify and quantify event related potentials (ERPs) during a gait cycle and to examine the effects of aging and dual tasking on these gait related potentials (GRPs). Methods 10 young and 10 older adults walked on a treadmill while wearing a wireless 20-channels EEG and accelerometers on the ankles. Each heel strike extracted from the accelerometers was used as an event to which the electrical brain activity pattern was locked. The subjects performed usual and dual task walking that included an auditory oddball task. GRPs amplitude and latency were computed, and a new measure referred to as Amplitude Pattern Consistency (APC) was developed to quantify the consistency of these GRP amplitudes within a gait cycle. The results were compared between and within groups using linear mixed model analysis. Results The electrical pattern during a gait cycle consisted of two main positive GRPs. Differences in these GRPs between young and older adults were observed in Pz and Cz. In Pz, older adults had higher GRPs amplitude (p = 0.006, p = 0.010), and in Cz lower APC (p = 0.025). Alterations were also observed between the walking tasks. Both groups showed shorter latency during oddball walking compared to usual walking in Cz (p = 0.040). In addition, the APC in Cz was correlated with gait speed (r = 0.599, p = 0.011) in all subjects and with stride time variability in the older adults (r = −0.703, p = 0.023). Conclusions This study is the first to define specific gait related potentials within a gait cycle using novel methods for quantifying waveforms. Our findings show the potential of this approach to be applied broadly to study the EEG during gait in a variety of contexts. The observed changes in GRPs with aging and walking task and the relationship between GRPs and gait may suggest the neurophysiologic foundation for studying walking and for developing new approaches for improving gait.

Background: The traditional evaluation of gait in the laboratory during structured testing has provided important insights, but is limited by its "snapshot" character and observation in an unnatural environment. Wearables enable monitoring of gait in real-world environments over a week. Initial findings show that in-lab and real-world measures differ. As a step towards better understanding these gaps, we directly compared in-lab usual-walking (UW) and dual-task walking (DTW) to daily-living measures of gait.Methods: In-lab gait features (e.g., gait speed, step regularity, and stride regularity) derived from UW and DTW were compared to the same gait features during daily-living in 150 elderly fallers (age: 76.5 ± 6.3 years, 37.6% men). In both settings, features were extracted from a lower-back accelerometer. In the real-world setting, subjects were asked to wear the device for 1 week and pre-processing detected 30-s daily-living walking bouts. A histogram of all walking bouts was determined for each walking feature for each subject and then each subject's typical (percentile 50, median), worst (percentile 10) and the best (percentile 90) values over the week were determined for each feature. Statistics of reliability were assessed using Intra-Class correlations and Bland-Altman plots.Results: As expected, in-lab gait speed, step regularity, and stride regularity were worse during DTW, compared to UW. In-lab gait speed, step regularity, and stride regularity during UW were significantly higher (i.e., better) than the typical daily-living values (p p p = 0.205, p = 0.053, p = 0.013 respectively). ICC assessment and Bland-Altman plots indicated that in-lab values do not reliably reflect the daily-walking values.Conclusions: Gait values measured during relatively long (30-s) daily-living walking bouts are more similar to the corresponding values obtained in the lab during dual-task walking, as compared to usual walking. Still, gait performance during most daily-living walking bouts is worse than that measured during usual and dual-tasking in the lab. The values measured in the lab do not reliably reflect daily-living measures. That is, an older adult's typical daily-living gait cannot be estimated by simply measuring walking in a structured, laboratory setting.

Body-fixed sensors (BFS), e.g., accelerometers worn for several days, can be used to augment the traditional clinical assessment. Long-term recordings obtained with BFS have been applied to study tremor, postural control, freezing of gait, turning abilities, motor response fluctuations and fall risk among older adults and patients with Parkinson's disease (PD). We aimed to test whether BFS-derived measures of transitions differ between patients with PD and healthy controls, and to evaluate whether there are differences among patients with mild PD, compared to more severe patients, and to controls. We also explored the added value of the metrics extracted from the sensor as compared to traditional testing in the lab. Ninety-nine patients with PD and 38 healthy older adults (HOA) participated in this study and wore a body-fixed sensor for 3 days. Walk-to-sit (n = 3286) and Sit-to-walk (n = 2858) transitions were analyzed and a machine learning algorithm was applied to distinguish between the groups. Significant differences in transitions were observed between PD patients and HOA, between mild and severe PD, and between mild PD and HOA, both in temporal and distribution features. The machine learning algorithm discriminated patients from HOA (accuracy = 92.3 %), mild from severe patients (accuracy = 89.8 %), and mild patients from HOA (accuracy = 85.9 %). These initial results suggest that body-fixed sensor-derived metrics of everyday transitions can characterize disease severity and differentiate mild PD patients from healthy older adults. Perhaps this approach can help with the integration of BFS into clinical care and the tracking of disease progression and the response to therapy.

Gait initiation, a task that demands the orchestration of cognitive and motor processes in order to transition from a standing posture to the onset of walking, can be partitioned into three phases: 1) The duration of motor planning extends from the signal to move until the onset of anticipatory postural adjustments (APA), time to APA. 2) The APA ends when the center-of-pressure returns to baseline; 3) Step duration, the “motor” implementation time, extends from the end of APA to heel-strike. To measure gait initiation metrics in large-scale studies, we developed an automated approach based on changes in the waveforms obtained from 3D accelerometer recordings from the lower back. To validate these metrics, we compared them with metrics extracted from concurrent instrumented gait-mat recordings in 41 healthy young adults (31.2 ± 5.0 yrs; %51 women) and 14 older adult women (mean age: 76.1 ± 6.3 yrs). Time to APA, APA duration, and step duration estimates derived from the wearable sensor and gait-mat were highly correlated (p

Objective To investigate EEG changes during an auditory odd-ball task while walking (dual-task) in young adults, older adults, and patients with Parkinson's disease. Methods 11 young adults, 10 older adults, and 10 patients with Parkinson’s disease (PD) performed an auditory oddball task during standing and walking on a treadmill, while wearing a wireless EEG cap. The amplitude and latency of P300 were compared between groups and within conditions using linear mix model analysis. Gait was evaluated using wearable sensors and cognition was assessed using the Color Trail Test. Results P300 latency became longer during walking in all groups (p = 0.005). During walking, older adults (p = 0.005) and patients with PD (p = 0.001) showed prolonged P300 latency compared to young adults. Significant task by group interaction was found in P300 amplitude (p = 0.008). Patients with PD demonstrated reduced P300 amplitude during walking compared to standing (p = 0.023). Among all subjects, better motor and cognitive performance correlated with shorter P300 latency (r = 0.457, p = 0.014 and r = 0.431, p = 0.040, respectively). Conclusions These findings provide direct evidence of the physiological recruitment of attentional networks during walking and their impact by ageing and disease. Significance This study is the first to report on changes in P300 latency and amplitude during dual-task oddball walking in older adults and patients with PD.

Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinson's disease. FoG is associated with falls and negatively impacts the patient's quality of life. Wearable systems that detect FoG in real time have been developed to help patients resume walking by means of rhythmic cueing. Current methods focus on detection, which require FoG events to happen first, while their prediction opens the road to preemptive cueing, which might help subjects to avoid freeze altogether. We analyzed electrocardiography (ECG) and skin-conductance (SC) data from 11 subjects who experience FoG in daily life, and found statistically significant changes in ECG and SC data just before the FoG episodes, compared to normal walking. Based on these findings, we developed an anomaly-based algorithm for predicting gait freeze from relevant SC features. We were able to predict 71.3% from 184 FoG with an average of 4.2 s before a freeze episode happened. Our findings enable the possibility of wearable systems, which predict with few seconds before an upcoming FoG from SC, and start external cues to help the user avoid the gait freeze.

Functional performance-based tests like the Timed Up and Go test (TUG) and its subtasks have been associated with fall risk, future disability, nursing home admission, and other poor outcomes in older adults. However, a single measurement in the laboratory may not fully reflect the subject's condition and everyday performance. To begin to validate an approach based on long-term, continuous monitoring, we investigated the sit-to-walk and walk-to-sit transitions performed spontaneously and naturally during daily living.Thirty young adults, 38 older adults, and 33 elderly (idiopathic) fallers were studied. After evaluating mobility and functional performance in the laboratory, participants wore an accelerometer on their lower back for 3 days. We analyzed the sit-to-walk and walk-to-sit transitions using temporal and distribution-related features. Machine learning algorithms assessed the feature set's ability to discriminate between the different cohorts.5,027 transitions were analyzed. Significant differences were observed between the young and older adults (p.044) and between the fallers and older adults (p.032). Machine learning algorithms classified the young and older adult with an accuracy of about 98% and the fallers and the older adults at 88%, which was better than the results achieved using traditional laboratory assessments (~72%).Features extracted from the multiple transitions recorded during daily living apparently reflect changes associated with aging and fall risk. Long-term monitoring of temporal features and their distribution may be helpful to provide a more complete and accurate assessment of the effects of aging and fall risk on daily function and mobility.

People with Parkinson’s disease (PD) suffer from declining mobility capabilities, which cause a prevalent risk of falling. Commonly, short periods of motor blocks occur during walking, known as freezing of gait (FoG). To slow the progressive decline of motor abilities, people with PD usually undertake stationary motor-training exercises in the clinics or supervised by physiotherapists. We present a wearable system for the support of people with PD and FoG. The system is designed for independent use. It enables motor training and gait assistance at home and other unsupervised environments. The system consists of three components. First, FoG episodes are detected in real time using wearable inertial sensors and a smartphone as the processing unit. Second, a feedback mechanism triggers a rhythmic auditory signal to the user to alleviate freeze episodes in an assistive mode. Third, the smartphone-based application features support for training exercises. Moreover, the system allows unobtrusive and long-term monitoring of the user’s clinical condition by transmitting sensing data and statistics to a telemedicine service. We investigate the at-home acceptance of the wearable system in a study with nine PD subjects. Participants deployed and used the system on their own, without any clinical support, at their homes during three protocol sessions in 1 week. Users’ feedback suggests an overall positive attitude toward adopting and using the system in their daily life, indicating that the system supports them in improving their gait. Further, in a data-driven analysis with sensing data from five participants, we study whether there is an observable effect on the gait during use of the system. In three out of five subjects, we observed a decrease in FoG duration distributions over the protocol days during gait-training exercises. Moreover, sensing data-driven analysis shows a decrease in FoG duration and FoG number in four out of five participants when they use the system as a gait-assistive tool during normal daily life activities at home.

Tripping over an obstacle is one of the most common causes of falls among older adults. However, the effects of aging, obstacle height and anticipation time on negotiation strategies have not been systematically evaluated.Twenty older adults (ages: 77.7±3.4years; 50% women) and twenty young adults (age: 29.3±3.8years; 50% women) walked through an obstacle course while negotiating anticipated and unanticipated obstacles at heights of 25mm and 75mm. Kinect cameras captured the: (1) distance of the subject's trailing foot before the obstacles, (2) distance of the leading foot after the obstacles, (3) clearance of the leading foot above the obstacles, and (4) clearance of the trailing foot above the obstacles. Linear-mix models assessed changes between groups and conditions.Older adults placed their leading foot closer to the obstacle after landing, compared to young adults (p0.001). This pattern was enhanced in high obstacles (group*height interaction, p=0.033). Older adults had lower clearance over the obstacles, compared to young adults (p=0.007). This was more pronounced during unanticipated obstacles (group*ART interaction, p=0.003). The distance of the leading foot and clearance of the trailing foot after the obstacles were correlated with motor, cognitive, and functional abilities.These findings suggest that there are age-related changes in obstacle crossing strategies that are dependent on the specific characteristics of the obstacle. The results have important implications for clinical practice, suggesting that functional exercise should include obstacle negotiation training with variable practice of height and available response times. Further studies are needed to better understand the effects of motor and cognitive abilities.

Freezing of gait (FOG) is an episodic gait disturbance that is commonly seen in Parkinson’s disease (PD). To date, treatment efficacy is limited. We tested the hypothesis that an intervention that utilizes motor learning provided through intensive cueing can alleviate this symptom. Fifteen subjects with PD suffering from FOG participated in a 6 week progressive motor learning program (three training sessions per week—open trial). A training session included FOG-provoking situations (e.g., turns). Prior to each presumed FOG provocation (e.g., just before a turn), rhythmic auditory stimulation (RAS) was elicited and the subject was trained to walk rhythmically, coordinate left–right stepping and to increase step size, utilizing the RAS cueing. Net training duration increased from week to week and secondary cognitive tasks while walking were added to increase FOG propensity. FOG symptom burden was assessed before, immediately, and 4 weeks after the training period. The mean number of FOG episodes (±SEM) per 10 m of walking in a standardized gait assessment decreased from 0.52 ± 0.29 in the pre-testing to 0.15 ± 0.04 in the post-testing (p < 0.05). The duration of FOG episodes decreased from 4.3 ± 2.1 to 2.6 ± 0.6 s (p < 0.05). Additional measures (e.g., FOG questionnaire, gait speed) varied in their responsiveness to the treatment. These effects were retained 4 weeks after the training. The results of this open label study support the possibility that a motor learning-based intervention is apparently effective in reducing FOG burden, suggesting that RAS can deliver ‘anti-FOG’ training.

Tandem walk (TW) is typically assessed by the time to complete the task and the number of missteps, however, these measures suffer from limitations and may not fully capture the range of performance in this task. We developed metrics of TW by using a body-fixed, wearable sensor in young and older adults. Healthy young men (n=40) and older adult men (n=362) were studied. While wearing a 3D accelerometer on their lower back, subjects performed three different tasks: TW, usual-walking, and quiet standing. The extracted measures for TW were: High-to-Low frequency band ratio from the power spectral density from the ML axis [nu], signal vector magnitude[g], step duration[s], sample entropy from ML, AP axis[nu] and CV[%]. All of the TW metrics were significantly different in the young and older men (p