Free-living Gait Research Articles

Cognitive and visual demands, but not gross motor demand, of concurrent smartphone use affect laboratory and free-living gait among young and older adults

BackgroundAs smartphones are an integral part of daily activities, understanding the underlying mechanism associated with concurrent cell phone use while walking may help reduce the risks of injury. Research questionThis study examined the effect of cognitive, visual, and gross motor demands while using a phone during gait among young and older adults in the laboratory and free-living environments. MethodsTwelve young and twelve older adults walked along a 10-m walkway under five conditions: single-task walking (Walk), walking and bi-manually holding a phone (Walk-Hold), walking while looking at a phone held in front of the participants (Walk-Look), walking while answering questions (Walk-Answer), and walking while texting (Walk-Text). All conditions were performed in laboratory and free-living environments. Gait velocity, step time, step length, and cadence were obtained using a smartphone with a built-in accelerometer attached to the body. The dual-task cost (DTC) was also assessed. A three-way ANOVA was utilized for all parameters. ResultsWhile no three-way interactions were found for any parameter, group × condition interactions were significant for gait velocity, step time, step length, cadence and their corresponding DTC. Decreased gait velocity, step length and cadence, with increased step time was demonstrated during Walk-Look, Walk-Answer, and Walk-Text, compared to Walk and Walk-Hold. While older adults markedly changed their gait during Walk-Answer and Walk-Text, these changes were less pronounced among young adults. SignificanceVisual and cognitive demand while concurrently using a phone influenced gait, especially among the elderly. Environment did not accentuate gait alterations during concurrent phone use. Therefore, smartphone technology should be developed to detect dual-task walking and temporarily modify functionality to reduce risk of injury from divided attention.

P 049 - Measuring gait parameters during community walking: Validation of a novel, low burden, long-lasting, free-living gait monitor

This paper describes the validation of an IMU-based gait monitor, developed for the long-term capture of gait parameters in the free-living community setting. Seven stroke survivors and 17 healthy participants performed a range of walking bouts upon a CAREN self-paced treadmill. The event timings of the device were compared to the gold-standard Vicon system and resulted in an average RMSE of 37.7 ms across all participants.

Free-living and laboratory gait characteristics in patients with multiple sclerosis.

BackgroundWearable sensors offer the potential to bring new knowledge to inform interventions in patients affected by multiple sclerosis (MS) by thoroughly quantifying gait characteristics and gait deficits from prolonged daily living measurements. The aim of this study was to characterise gait in both laboratory and daily life conditions for a group of patients with moderate to severe ambulatory impairment due to MS. To this purpose, algorithms to detect and characterise gait from wearable inertial sensors data were also validated.MethodsFourteen patients with MS were divided into two groups according to their disability level (EDSS 6.5–6.0 and EDSS 5.5–5.0, respectively). They performed both intermittent and continuous walking bouts (WBs) in a gait laboratory wearing waist and shank mounted inertial sensors. An algorithm (W-CWT) to estimate gait events and temporal parameters (mean and variability values) using data recorded from the waist mounted sensor (Dynaport, Mc Roberts) was tested against a reference algorithm (S-REF) based on the shank-worn sensors (OPAL, APDM). Subsequently, the accuracy of another algorithm (W-PAM) to detect and classify WBs was also tested. The validated algorithms were then used to quantify gait characteristics during short (sWB, 5–50 steps), intermediate (iWB, 51–100 steps) and long (lWB, >100 steps) daily living WBs and laboratory walking. Group means were compared using a two-way ANOVA.ResultsW-CWT compared to S-REF showed good gait event accuracy (0.05–0.10 s absolute error) and was not influenced by disability level. It slightly overestimated stride time in intermittent walking (0.012 s) and overestimated highly variability of temporal parameters in both intermittent (17.5%–58.2%) and continuous walking (11.2%–76.7%). The accuracy of W-PAM was speed-dependent and decreased with increasing disability. The ANOVA analysis showed that patients walked at a slower pace in daily living than in the laboratory. In daily living gait, all mean temporal parameters decreased as the WB duration increased. In the sWB, the patients with a lower disability score showed, on average, lower values of the temporal parameters. Variability decreased as the WB duration increased.ConclusionsThis study validated a method to quantify walking in real life in people with MS and showed how gait characteristics estimated from short walking bouts during daily living may be the most informative to quantify level of disability and effects of interventions in patients moderately affected by MS. The study provides a robust approach for the quantification of recognised clinically relevant outcomes and an innovative perspective in the study of real life walking.

Gait in Mild Alzheimer's Disease: Feasibility of Multi-Center Measurement in the Clinic and Home with Body-Worn Sensors: A Pilot Study.

Gait is emerging as a potential diagnostic tool for cognitive decline. The 'Deep and Frequent Phenotyping for Experimental Medicine in Dementia Study' (D&FP) is a multicenter feasibility study embedded in the United Kingdom Dementia Platform designed to determine participant acceptability and feasibility of extensive and repeated phenotyping to determine the optimal combination of biomarkers to detect disease progression and identify early risk of Alzheimer's disease (AD). Gait is included as a clinical biomarker. The tools to quantify gait in the clinic and home, and suitability for multi-center application have not been examined. Six centers from the National Institute for Health Research Translational Research Collaboration in Dementia initiative recruited 20 individuals with early onset AD. Participants wore a single wearable (tri-axial accelerometer) and completed both clinic-based and free-living gait assessment. A series of macro (behavioral) and micro (spatiotemporal) characteristics were derived from the resultant data using previously validated algorithms. Results indicate good participant acceptability, and potential for use of body-worn sensors in both the clinic and the home. Recommendations for future studies have been provided. Gait has been demonstrated to be a feasible and suitable measure, and future research should examine its suitability as a biomarker in AD.

Analysis of Free-Living Gait in Older Adults With and Without Parkinson's Disease and With and Without a History of Falls: Identifying Generic and Disease-Specific Characteristics.

Falls are associated with gait impairments in older adults (OA) and Parkinson's disease (PD). Current approaches for evaluating falls risk are based on self-report or one-time assessment and may be suboptimal. Wearable technology allows gait to be measured continuously in free-living conditions. The aim of this study was to explore generic and specific associations in free-living gait in fallers and nonfallers with and without PD. Two hundred and seventy-seven fallers (155 PD, 122 OA) who fell twice or more in the previous 6 months and 65 nonfallers (15 PD, 50 OA) were tested. Free-living gait was characterized as the volume, pattern, and variability of ambulatory bouts (Macro), and 14 discrete gait characteristics (Micro). Macro and Micro variables were quantified from free-living data collected using an accelerometer positioned on the low back for one week. Macro variables showed that fallers walked with shorter and less variable ambulatory bouts than nonfallers, independent of pathology. Micro variables within ambulatory bouts showed fallers walked with slower, shorter and less variable steps than nonfallers. Significant interactions showed disease specific differences in variability with PD fallers demonstrating greater variability (step length) and OA fallers less variability (step velocity) than their nonfaller counterparts (p < 0.004). Common and disease-specific changes in free-living Macro and Micro gait highlight generic and selective targets for intervention depending on type of faller (OA-PD). Our findings support free-living monitoring to enhance assessment. Future work is needed to confirm the optimal battery of measures, sensitivity to change and value for fall prediction.

Monitoring of Gait Quality in Patients With Chronic Pain of Lower Limbs.

Severe injuries of lower extremities often lead to chronic pain and reduced walking abilities. We postulated that measuring free-living gait can provide further information about walking ability in complement to clinical evaluations. We sought to validate a method that characterizes free gaits with a wearable sensor. Over one week, 81 healthy controls (HC) and 66 chronic lower limb pain patients (CLLPP) hospitalized for multidisciplinary rehabilitation wore a simple accelerometer (Actigraph). In the acceleration signals, steady 1-min walks detected numbered 7,835 (5,085 in CLLPP and 2,750 in HC). Five gait quality measures were assessed: movement intensity, cadence, stride regularity, and short-term and long-term local dynamic stability. Gait quality variables differed significantly between CLLPP and HC (4%-26%). Intraclass correlation coefficients revealed moderate to high repeatability (0.71-0.91), which suggests that seven days of measurement are sufficient to assess average gait patterns. Regression analyses showed significant association (R2 = 0.44) between the gait quality variables and a clinical evaluation of walking ability, i.e., the 6-min walk test. Overall, the results show that the method is easy to implement, valid (high concurrent validity), and reliable to assess walking abilities ecologically.

Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models.

The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living environment, without constant supervision from researchers, sensor-based gait features are susceptible to variation from confounding factors such as gait speed and mounting uncertainty, which are challenging to control or estimate. This paper is one of the first attempts in the field to tackle such challenges using statistical modeling. By accepting the uncertainties and variation associated with wearable sensor-based gait data, we shift our efforts from detecting and correcting those variations to modeling them statistically. From gait data collected on one healthy, non-elderly subject during 48 full-factorial trials, we identified four major sources of variation, and quantified their impact on one gait outcome—range per cycle—using a random effects model and a fixed effects model. The methodology developed in this paper lays the groundwork for a statistical framework to account for sources of variation in wearable gait data, thus facilitating informative statistical inference for free-living gait analysis.

Allometrically Scaled Children's Clinical and Free-Living Ambulatory Behavior.

This study aimed to compare clinical and free-living walking cadence in school-age children and to examine how the allometric scaling of leg length variability affects objective ambulatory activity assessment. A total of 375 children (154 boys and 221 girls, 9-11 yr old) completed GAITRite-determined slow, normal, and fast walks and wore accelerometers for 1 wk. Dependent variables from clinical assessment included gait speed, cadence, and step length, whereas steps per day, peak 1-min cadence, and peak 60-min cadence were assessed during free living. Analogous allometrically scaled variables were used to account for leg length differences. Free-living times above clinically determined individualized slow, normal, and fast cadence values were calculated. Differences in dependent variables between sex and sex-specific leg length tertiles were assessed. Clinically assessed cadence (mean ± SD) was 90.9 ± 15.2 (slow), 113.8 ± 12.9 (normal), and 148.9 ± 20.9 (fast) steps per minute, respectively. During free living, participants accumulated 8651 ± 2259 steps per day. Peak 1-min cadence was 113.4 ± 12.4 steps per minute and peak 60-min cadence was 60.1 ± 11.4 steps per minute. Allometrically scaling gait variables to leg length eliminated the previously significant leg length effect observed in both clinical and free-living gait variables but did not affect the observation that girls exhibited lower levels of free-living ambulatory behavior measured by mean steps per day. On average, all groups spent <15 min·d above clinically determined slow cadence; this was unaffected by leg length. Allometrically scaling gait variables to leg length significantly affected the assessment of ambulatory behavior, such that different leg length groups appear to walk in a dynamically similar manner. Leg length effects on free-living ambulatory behavior were also eliminated by implementing estimates of time spent above individualized cadence cut points derived from clinical gait assessment.

A model of free-living gait: A factor analysis in Parkinson’s disease

IntroductionGait is a marker of global health, cognition and falls risk. Gait is complex, comprised of multiple characteristics sensitive to survival, age and pathology. Due to covariance amongst characteristics, conceptual gait models have been established to reduce redundancy and aid interpretation. Previous models have been derived from laboratory gait assessments which are costly in equipment and time. Body-worn monitors (BWM) allow for free-living, low-cost and continuous gait measurement and produce similar covariant gait characteristics. A BWM gait model from both controlled and free-living measurement has not yet been established, limiting utility. Methods103 control and 67 PD participants completed a controlled laboratory assessment; walking for two minutes around a circuit wearing a BWM. 89 control and 58 PD participants were assessed in free-living, completing normal activities for 7 days wearing a BWM. Fourteen gait characteristics were derived from the BWM, selected according to a previous model. Principle component analysis derived factor loadings of gait characteristics. ResultsFour gait domains were derived for both groups and conditions; pace, rhythm, variability and asymmetry. Domains totalled 84.84% and 88.43% of variance for controlled and 90.00% and 93.03% of variance in free-living environments for control and PD participants respectively. Gait characteristic loading was unambiguous for all characteristics apart from gait variability which demonstrated cross-loading for both groups and environments. The model was highly congruent with the original model. ConclusionsThe conceptual gait models remained stable using a BWM in controlled and free-living environments. The model became more discrete supporting utility of the gait model for free-living gait.

Gait event detection in laboratory and real life settings: Accuracy of ankle and waist sensor based methods

Wearable sensors technology based on inertial measurement units (IMUs) is leading the transition from laboratory-based gait analysis, to daily life gait monitoring. However, the validity of IMU-based methods for the detection of gait events has only been tested in laboratory settings, which may not reproduce real life walking patterns. The aim of this study was to evaluate the accuracy of two algorithms for the detection of gait events and temporal parameters during free-living walking, one based on two shank-worn inertial sensors, and the other based on one waist-worn sensor. The algorithms were applied to gait data of ten healthy subjects walking both indoor and outdoor, and completing protocols that entailed both straight supervised and free walking in an urban environment. The values obtained from the inertial sensors were compared to pressure insoles data. The shank-based method showed very accurate initial contact, stride time and step time estimation (<14ms error). Accuracy of final contact timings and stance time was lower (28–51ms error range). The error of temporal parameter variability estimates was in the range 0.09–0.89%. The waist method failed to detect about 1% of the total steps and performed worse than the shank method, but the temporal parameter estimation was still satisfactory. Both methods showed negligible differences in their accuracy when the different experimental conditions were compared, which suggests their applicability in the analysis of free-living gait.

Free-living gait characteristics in ageing and Parkinson's disease: impact of environment and ambulatory bout length.

BackgroundGait is emerging as a powerful diagnostic and prognostic tool, and as a surrogate marker of disease progression for Parkinson’s disease (PD). Accelerometer-based body worn monitors (BWMs) facilitate the measurement of gait in clinical environments. Moreover they have the potential to provide a more accurate reflection of gait in the home during habitual behaviours. Emerging research suggests that measurement of gait using BWMs is feasible but this has not been investigated in depth. The aims of this study were to explore (i) the impact of environment and (ii) ambulatory bout (AB) length on gait characteristics for discriminating between people with PD and age-matched controls.MethodsFourteen clinically relevant gait characteristics organised in five domains (pace, variability, rhythm, asymmetry, postural control) were quantified using laboratory based and free-living data collected over 7 days using a BWM placed on the lower back in 47 PD participants and 50 controls.ResultsFree-living data showed that both groups walked with decreased pace and increased variability, rhythm and asymmetry compared to walking in the laboratory setting. Four of the 14 gait characteristics measured in free-living conditions were significantly different between controls and people with PD compared to two measured in the laboratory. Between group differences depended on bout length and were more apparent during longer ABs. ABs ≤ 10s did not discriminate between groups. Medium to long ABs highlighted between-group significant differences for pace, rhythm and asymmetry. Longer ABs should therefore be taken into account when evaluating gait characteristics in free-living conditions.ConclusionThis study provides encouraging results to support the use of a single BWM for free-living gait evaluation in people with PD with potential for research and clinical application.

A study of position independent algorithms for phone-based gait frequency detection.

Estimating gait frequency is an important component in the detection and diagnosis of various medical conditions. Smartphone-based kinematic sensors offer a window of opportunity in free-living gait frequency estimation. The main issue with smartphone-based gait frequency estimation algorithms is how to adjust for variations in orientation and location of the phone on the human body. While numerous algorithms have been implemented to account for these differences, little work has been done in comparing these algorithms. In this study, we compare various position independent algorithms to determine which are more suited to robust gait frequency estimation. Using sensor data collected from volunteers walking with a smartphone, we examine the effect of using three different time series with the magnitude, weighted sum, and closest vertical component algorithms described in the paper. We also test two different methods of extracting step frequency: time domain peak counting and spectral analysis. The results show that the choice of time series does not significantly affect the accuracy of frequency measurements. Furthermore, both time domain and spectral approaches show comparable results. However, time domain approaches are sensitive to false-positives while spectral approaches require a minimum set of repetitive measurements. Our study suggests a hybrid approach where both time-domain and spectral approaches be used together to complement each other's shortcomings.

Electrocardiogram Signal Analysis for Physical Motion Based on Wavelet Approach

In this study, a portable, low-cost system, Portable Motion Analyzer (PMA), is introduced to obtain data from daily physical motions, such as ECG, heart rate signals, as well as kinetic information of motion and free-living gait. It can gather, process and analysis the signals from multiple input channels. To process these signals, digital filtering and wavelet analysis is used for quantitative analysis, which can de-noise, de-composite and reconstruct the signals. Similar to the Fast Fourier Transformation (FFT) in the Fourier, Mallat algorithm can realize the decomposition and reconstruction of the signal according to the coefficient. Experiments show that the system can effectively de-noise analysis of the data from MIT-BIH arrhythmia database and analysis the signals of body subjected to the shock of ground. It is proved efficient and stable in the most practical scenarios.