Gait parameter estimation from a miniaturized ear-worn sensor using singular spectrum analysis and longest common subsequence

EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors

Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user's daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid's integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.

Detection of Intermittent Claudication from Smartphone Inertial Data in Community Walks Using Machine Learning Classifiers

Peripheral arterial disease (PAD) causes blockage of the arteries, altering the blood flow to the lower limbs. This blockage can cause the individual with PAD to feel severe pain in the lower limbs. The main contribution of this research is the discovery of a solution that allows the automatic detection of the onset of claudication based on data analysis from patients' smartphones. For the data-collection procedure, 40 patients were asked to walk with a smartphone on a thirty-meter path, back and forth, for six minutes. Each patient conducted the test twice on two different days. Several machine learning models were compared to detect the onset of claudication on two different datasets. The results suggest that we can identify the onset of claudication using inertial sensors with a best case accuracy of 92.25% for the Extreme Gradient Boosting model.

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.

Reliability and Validity of Running Cadence and Stance Time Derived from Instrumented Wireless Earbuds

Instrumented earbuds equipped with accelerometers were developed in response to limitations of currently used running wearables regarding sensor location and feedback delivery. The aim of this study was to assess test-retest reliability, face validity and concurrent validity for cadence and stance time in running. Participants wore an instrumented earbud (new method) while running on a treadmill with embedded force-plates (well-established method). They ran at a range of running speeds and performed several instructed head movements while running at a comfortable speed. Cadence and stance time were derived from raw earbud and force-plate data and compared within and between both methods using t-tests, ICC and Bland-Altman analysis. Test-retest reliability was good-to-excellent for both methods. Face validity was demonstrated for both methods, with cadence and stance time varying with speed in to-be-expected directions. Between-methods agreement for cadence was excellent for all speeds and instructed head movements. For stance time, agreement was good-to-excellent for all conditions, except while running at 13 km/h and shaking the head. Overall, the measurement of cadence and stance time using an accelerometer embedded in a wireless earbud showed good test-retest reliability, face validity and concurrent validity, indicating that instrumented earbuds may provide a promising alternative to currently used wearable systems.

Head Trajectory Diagrams for Gait Symmetry Analysis Using a Single Head-Worn IMU

Gait symmetry analysis plays an important role in the diagnosis and rehabilitation of pathological gait. Recently, wearable devices have also been developed for simple gait analysis solutions. However, measurement in clinical settings can differ from gait in daily life, and simple wearable devices are restricted to a few parameters, providing one-sided trajectories of one arm or leg. Therefore, head-worn devices with sensors (e.g., earbuds) should be considered to analyze gait symmetry because the head sways towards the left and right side depending on steps. This paper proposed new visualization methods using head-worn sensors, able to facilitate gait symmetry analysis outside as well as inside. Data were collected with an inertial measurement unit (IMU) based motion capture system when twelve participants walked on the 400-m running track. From head trajectories on the transverse and frontal plane, three types of diagrams were displayed, and five concepts of parameters were measured for gait symmetry analysis. The mean absolute percentage error (MAPE) of step counting was lower than 0.65%, representing the reliability of measured parameters. The methods enable also left-right step recognition (MAPE ≤ 2.13%). This study can support maintenance and relearning of a balanced healthy gait in various areas with simple and easy-to-use devices.

A Singular Spectrum Analysis-Based Data-Driven Technique for the Removal of Cardiogenic Oscillations in Esophageal Pressure Signals

Objective: Assessing the respiratory and lung mechanics of the patients in intensive care units is of utmost need in order to guide the management of ventilation support. The esophageal pressure (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document}) signal is a minimally invasive measure, which portrays the mechanics of the lung and the pattern of breathing. Because of the close proximity of the lung to the beating heart inside the thoracic cavity, the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signals always get contaminated with that of the oscillatory-pressure-signal of the heart, which is known as the cardiogenic oscillation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document}) signal. However, the area of research addressing the removal of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document} from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal is still lagging behind. Methods and results: This paper presents a singular spectrum analysis-based high-efficient, adaptive and robust technique for the removal of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document} from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal utilizing the inherent periodicity and morphological property of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal. The performance of the proposed technique is tested on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signals collected from the patients admitted to the intensive care unit, cadavers, and also on synthetic \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signals. The efficiency of the proposed technique in removing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document} from the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal is quantified through both qualitative and quantitative measures, and the mean opinion scores of the denoised \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal fall under the categories ‘very good’ as per the subjective measure. Conclusion and clinical impact: The proposed technique: (1) does not follow any predefined mathematical model and hence, it is data-driven, (2) is adaptive to the sampling rate, and (3) can be adapted for denoising other biomedical signals which exhibit periodic or quasi-periodic nature.

A novel gait parameter estimation method for healthy adults and postoperative patients with an ear-worn sensor

OBJECTIVE

Gait analysis helps to assess recovery during rehabilitation. Previous gait analysis studies are primarily applicable to healthy subjects or to postoperative patients. The purpose of this paper is to construct a new gait parameter estimation platform based on an ear-worn activity recognition (e-AR) sensor, which can be used for both normal and pathological gait signals.

APPROACH

Thirty healthy adults and eight postoperative patients participated in the experiment. A method based on singular spectrum analysis (SSA) and iterative mean filtering (IMF) is proposed to detect gait events and estimate three key gait parameters, i.e. stride time, swing time, and stance time.

MAIN RESULTS

Experimental results show that the estimated gait parameters provided by the proposed method are very close to the gait parameters provided by the gait assessment system. For normal gait signals, the average absolute errors of stride, swing, and stance time are 27.8 ms, 35.8 ms, and 37.5 ms, respectively. For pathological gait signals, the average absolute error of stride time is 32.1 ms.

SIGNIFICANCE

The proposed parameter estimation method can be applied to both general analysis for healthy subjects and rehabilitation evaluation for postoperative patients. The convenience and comfort of the ear-worn sensor increase its potential for practical applications.

Validity and reliability of wearable inertial sensors in healthy adult walking: a systematic review and meta-analysis

BACKGROUND

Inertial measurement units (IMUs) offer the ability to measure walking gait through a variety of biomechanical outcomes (e.g., spatiotemporal, kinematics, other). Although many studies have assessed their validity and reliability, there remains no quantitive summary of this vast body of literature. Therefore, we aimed to conduct a systematic review and meta-analysis to determine the i) concurrent validity and ii) test-retest reliability of IMUs for measuring biomechanical gait outcomes during level walking in healthy adults.

METHODS

Five electronic databases were searched for journal articles assessing the validity or reliability of IMUs during healthy adult walking. Two reviewers screened titles, abstracts, and full texts for studies to be included, before two reviewers examined the methodological quality of all included studies. When sufficient data were present for a given biomechanical outcome, data were meta-analyzed on Pearson correlation coefficients (r) or intraclass correlation coefficients (ICC) for validity and reliability, respectively. Alternatively, qualitative summaries of outcomes were conducted on those that could not be meta-analyzed.

RESULTS

A total of 82 articles, assessing the validity or reliability of over 100 outcomes, were included in this review. Seventeen biomechanical outcomes, primarily spatiotemporal parameters, were meta-analyzed. The validity and reliability of step and stride times were found to be excellent. Similarly, the validity and reliability of step and stride length, as well as swing and stance time, were found to be good to excellent. Alternatively, spatiotemporal parameter variability and symmetry displayed poor to moderate validity and reliability. IMUs were also found to display moderate reliability for the assessment of local dynamic stability during walking. The remaining biomechanical outcomes were qualitatively summarized to provide a variety of recommendations for future IMU research.

CONCLUSIONS

The findings of this review demonstrate the excellent validity and reliability of IMUs for mean spatiotemporal parameters during walking, but caution the use of spatiotemporal variability and symmetry metrics without strict protocol. Further, this work tentatively supports the use of IMUs for joint angle measurement and other biomechanical outcomes such as stability, regularity, and segmental accelerations. Unfortunately, the strength of these recommendations are limited based on the lack of high-quality studies for each outcome, with underpowered and/or unjustified sample sizes (sample size median 12; range: 2-95) being the primary limitation.

A singular spectrum analysis-based model-free electrocardiogram denoising technique

BACKGROUND AND OBJECTIVE

An efficient and robust electrocardiogram (ECG) denoising technique caters three-fold benefits in the subsequent processing steps: first, it helps improving the accuracy of extracted features. Second, the improved accuracy in the extracted features enhances the performance as well as the reliability of computerised cardiovascular-disease diagnosis systems, and third, it also makes the interpretation task easier for the clinicians. Albeit a number of ECG denoising techniques are proposed in the literature, but most of these techniques suffer from one or more of the following drawbacks: i) model or function dependency, ii) sampling-rate dependency, or iii) high time-complexity.

METHODS

This paper presents a singular spectrum analysis (SSA)-based ECG denoising technique addressing most of these afore-mentioned shortcomings. First, a trajectory matrix of dimension K × L is formed using the original one-dimensional ECG signal of length N. In SSA operation the parameter L, which is denoted as the window-length, plays a very important role and is related to the sampling frequency of the signal. In this research the value of L is calculated dynamically based on the morphological property of the ECG signal. Then, the matrix is decomposed using singular value decomposition technique, and the principal components (PC) of the original ECG signal are computed. Next, the reconstructed components (RC) are calculated from the PCs, and all the RCs are filtered through Butterworth bandpass and notch filters. An optimum number of filtered RCs are retained based on their significance. Finally, these retained RCs are summed up to obtain the denoised ECG signal.

RESULTS

Evaluation result shows that the proposed technique outperforms state-of-the-art ECG denoising methods; in particular, the mean opinion score of the denoised signal falls under the category 'very good' as per the gold standard subjective measure.

CONCLUSIONS

Both the quantitative and qualitative distortion measure metrics show that the proposed ECG denoising technique is robust enough to filter various noises present in the signal without jeopardizing the clinical content. The proposed technique could be adapted for denoising other biomedical signals exhibiting periodic or quasi-periodic nature such as photoplethysmogram and esophageal pressure signal.

From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology

Mood disorders affect more than 300 million people worldwide and can cause devastating consequences. Elderly people and patients with neurological conditions are particularly susceptible to depression. Gait and body movements can be affected by mood disorders, and thus they can be used as a surrogate sign, as well as an objective index for pervasive monitoring of emotion and mood disorders in daily life. Here we review evidence that demonstrates the relationship between gait, emotions and mood disorders, highlighting the potential of a multimodal approach that couples gait data with physiological signals and home-based monitoring for early detection and management of mood disorders. This could enhance self-awareness, enable the development of objective biomarkers that identify high risk subjects and promote subject-specific treatment.

Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions

Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations.

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.

Evaluating physical function and activity in the elderly patient using wearable motion sensors

Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand.

Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures.

In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts.

In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the ‘micro-level’ of activity such as step frequency or sit-stand duration.

Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability remain difficult, calling for consensus or standardisation.

Cite this article: Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016;1:112–120. DOI: 10.1302/2058-5241.1.160022.

Towards Photoplethysmography-Based Estimation of Instantaneous Heart Rate During Physical Activity

OBJECTIVE

Recently numerous methods have been proposed for estimating average heart rate using photoplethysmography (PPG) during physical activity, overcoming the significant interference that motion causes in PPG traces. We propose a new algorithm framework for extracting instantaneous heart rate from wearable PPG and Electrocardiogram (ECG) signals to provide an estimate of heart rate variability during exercise.

METHODS

For ECG signals, we propose a new spectral masking approach which modifies a particle filter tracking algorithm, and for PPG signals constrains the instantaneous frequency obtained from the Hilbert transform to a region of interest around a candidate heart rate measure. Performance is verified using accelerometry and wearable ECG and PPG data from subjects while biking and running on a treadmill.

RESULTS

Instantaneous heart rate provides more information than average heart rate alone. The instantaneous heart rate can be extracted during motion to an accuracy of 1.75 beats per min (bpm) from PPG signals and 0.27 bpm from ECG signals.

CONCLUSION

Estimates of instantaneous heart rate can now be generated from PPG signals during motion. These estimates can provide more information on the human body during exercise.

SIGNIFICANCE

Instantaneous heart rate provides a direct measure of vagal nerve and sympathetic nervous system activity and is of substantial use in a number of analyzes and applications. Previously it has not been possible to estimate instantaneous heart rate from wrist wearable PPG signals.

Single-Camera-Based Method for Step Length Symmetry Measurement in Unconstrained Elderly Home Monitoring

OBJECTIVE

single-camera-based gait monitoring is unobtrusive, inexpensive, and easy-to-use to monitor daily gait of seniors in their homes. However, most studies require subjects to walk perpendicularly to camera's optical axis or along some specified routes, which limits its application in elderly home monitoring. To build unconstrained monitoring environments, we propose a method to measure step length symmetry ratio (a useful gait parameter representing gait symmetry without significant relationship with age) from unconstrained straight walking using a single camera, without strict restrictions on walking directions or routes.

METHODS

according to projective geometry theory, we first develop a calculation formula of step length ratio for the case of unconstrained straight-line walking. Then, to adapt to general cases, we propose to modify noncollinear footprints, and accordingly provide general procedure for step length ratio extraction from unconstrained straight walking.

RESULTS

Our method achieves a mean absolute percentage error (MAPE) of 1.9547% for 15 subjects' normal and abnormal side-view gaits, and also obtains satisfactory MAPEs for non-side-view gaits (2.4026% for 45°-view gaits and 3.9721% for 30°-view gaits). The performance is much better than a well-established monocular gait measurement system suitable only for side-view gaits with a MAPE of 3.5538%.

CONCLUSION

Independently of walking directions, our method can accurately estimate step length ratios from unconstrained straight walking.

SIGNIFICANCE

This demonstrates our method is applicable for elders' daily gait monitoring to provide valuable information for elderly health care, such as abnormal gait recognition, fall risk assessment, etc.

OBJECTIVE

single-camera-based gait monitoring is unobtrusive, inexpensive, and easy-to-use to monitor daily gait of seniors in their homes. However, most studies require subjects to walk perpendicularly to camera's optical axis or along some specified routes, which limits its application in elderly home monitoring. To build unconstrained monitoring environments, we propose a method to measure step length symmetry ratio (a useful gait parameter representing gait symmetry without significant relationship with age) from unconstrained straight walking using a single camera, without strict restrictions on walking directions or routes.

METHODS

according to projective geometry theory, we first develop a calculation formula of step length ratio for the case of unconstrained straight-line walking. Then, to adapt to general cases, we propose to modify noncollinear footprints, and accordingly provide general procedure for step length ratio extraction from unconstrained straight walking.

RESULTS

Our method achieves a mean absolute percentage error (MAPE) of 1.9547% for 15 subjects' normal and abnormal side-view gaits, and also obtains satisfactory MAPEs for non-side-view gaits (2.4026% for 45°-view gaits and 3.9721% for 30°-view gaits). The performance is much better than a well-established monocular gait measurement system suitable only for side-view gaits with a MAPE of 3.5538%.

CONCLUSION

Independently of walking directions, our method can accurately estimate step length ratios from unconstrained straight walking.

SIGNIFICANCE

This demonstrates our method is applicable for elders' daily gait monitoring to provide valuable information for elderly health care, such as abnormal gait recognition, fall risk assessment, etc.

Implementation of objective activity monitoring to supplement the interpretation of ambulatory esophageal PH investigations

Conventional catheter-based systems used for ambulatory esophageal pH monitoring have been reported to affect patient behavior. As physical activity has been associated with gastroesophageal reflux disease (GERD), there is a risk that abnormal behavior will degrade the value of this diagnostic investigation and consequent management strategies. The aim of this study was to quantify the effect of conventional pH monitoring on behavior and to investigate the temporal association between activity and reflux. A total of 20 patients listed for 24 hours pH monitoring underwent activity monitoring using a lightweight ear-worn accelerometer (e-AR sensor, Imperial College London) 2 days prior to, and during their investigation. PH was measured and recorded using a conventional nasogastric catheter and waist-worn receiver. Daily activity levels, including subject-specific activity intensity quartiles, were calculated and compared. Physical activity was added to the standard pH output to supplement interpretation. Average patient activity levels decreased by 26.5% during pH monitoring (range -4.5 to 51.0%, P = 0.036). High-intensity activity decreased by 24.4% (range -4.0 to 75.6%, P = 0.036), and restful activity increased on average by 34% although this failed to reach statistical significance (-24.0 to 289.2%, P = 0.161). Some patients exhibited consistent associations between bouts of activity and acidic episodes. The results of this study support the previously reported reduction in activity during ambulatory esophageal pH monitoring, with the added reliability of objective data. In the absence of more pervasive pH monitoring systems (e.g. wireless), quantifying activity changes in the setting of activity-induced reflux might guide the physicians' interpretation of patient DeMeester scores resulting in more appropriate management of GERD.

Eight-Week Remote Monitoring Using a Freely Worn Device Reveals Unstable Gait Patterns in Older Fallers

OBJECTIVES

Develop algorithms to detect gait impairments remotely using data from freely worn devices during long-term monitoring. Identify statistical models that describe how gait performances are distributed over several weeks. Determine the data window required to reliably assess an increased propensity for falling.

METHODS

1085 days of walking data were collected from eighteen independent-living older people (mean age 83 years) using a freely worn pendant sensor (housing a triaxial accelerometer and pressure sensor). Statistical distributions from several accelerometer-derived gait features (encompassing quantity, exposure, intensity, and quality) were compared for those with and without a history of falling.

RESULTS

Participants completed more short walks relative to long walks, as approximated by a power law. Walks less than 13.1 s comprised 50% of exposure to walking-related falls. Daily-life cadence was bimodal and step-time variability followed a log-normal distribution. Fallers took significantly fewer steps per walk and had relatively more exposure from short walks and greater mode of step-time variability.

CONCLUSIONS

Using a freely worn device and wavelet-based analysis tools allowed long-term monitoring of walks greater than or equal to three steps. In older people, short walks constitute a large proportion of exposure to falls. To identify fallers, mode of variability may be a better measure of central tendency than mean of variability. A week's monitoring is sufficient to reliably assess the long-term propensity for falling.

SIGNIFICANCE

Statistical distributions of gait performances provide a reference for future wearable device development and research into the complex relationships between daily-life walking patterns, morbidity, and falls.

Motion tracking and gait feature estimation for recognising Parkinson's disease using MS Kinect

Background

Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson’s disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space.

Methods

The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson’s disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data.

Results

The main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson’s disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications.

Conclusions

Discussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.

Return of functional mobility after an open tibial fracture: a sensor-based longitudinal cohort study using the Hamlyn Mobility Score

In this study we quantified and characterised the return of functional mobility following open tibial fracture using the Hamlyn Mobility Score. A total of 20 patients who had undergone reconstruction following this fracture were reviewed at three-month intervals for one year. An ear-worn movement sensor was used to assess their mobility and gait. The Hamlyn Mobility Score and its constituent kinematic features were calculated longitudinally, allowing analysis of mobility during recovery and between patients with varying grades of fracture. The mean score improved throughout the study period. Patients with more severe fractures recovered at a slower rate; those with a grade I Gustilo-Anderson fracture completing most of their recovery within three months, those with a grade II fracture within six months and those with a grade III fracture within nine months. Analysis of gait showed that the quality of walking continued to improve up to 12 months post-operatively, whereas the capacity to walk, as measured by the six-minute walking test, plateaued after six months. Late complications occurred in two patients, in whom the trajectory of recovery deviated by > 0.5 standard deviations below that of the remaining patients. This is the first objective, longitudinal assessment of functional recovery in patients with an open tibial fracture, providing some clarification of the differences in prognosis and recovery associated with different grades of fracture.

A wearable mobility assessment device for total knee replacement: A longitudinal feasibility study

BACKGROUND

Total knee replacement currently lacks robust indications and objective follow-up metrics. Patients and healthcare staff are under-equipped to optimise outcomes. This study aims to investigate the feasibility of using an ear-worn motion sensor (e-AR, Imperial College London) to conduct objective, home-based mobility assessments in the peri-operative setting.

METHODS

Fourteen patients on the waiting list for knee replacement, and 15 healthy subjects, were recruited. Pre-operatively, and at 1, 3, 6, 12 and 24 weeks post-operatively, patients underwent functional mobility testing (Timed Up and Go), knee examination (including range of motion), and an activity protocol whilst wearing the e-AR sensor. Features extracted from sensor motion data were used to assess patient performance and predict patients' recovery phase.

RESULTS

Sensor-derived peri-operative mobility trends correlated with clinical measures in several activities, allowing functional recovery of individual subjects to be profiled and compared, including the detection of a complication. Sensor data features enabled classification of subjects into normal, pre-operative and 24-week post-operative groups with 89% (median) accuracy. Classification accuracy was reduced to 69% when including all time intervals.

DISCUSSION

This study demonstrates a novel, objective method of assessing peri-operative mobility, which could be used to supplement surgical decision-making and facilitate community-based follow-up.

Synchronous wearable wireless body sensor network composed of autonomous textile nodes

A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system.