Discriminative Information Added by Wearable Sensors for Early Screening - a Case Study on Diabetic Peripheral Neuropathy (original) (raw)
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Single Sensor Gait Analysis to Detect Diabetic Peripheral Neuropathy: A Proof of Principle Study
Diabetes & metabolism journal, 2018
This study explored the potential utility of gait analysis using a single sensor unit (inertial measurement unit [IMU]) as a simple tool to detect peripheral neuropathy in people with diabetes. Seventeen people (14 men) aged 63±9 years (mean±SD) with diabetic peripheral neuropathy performed a 10-m walk test instrumented with an IMU on the lower back. Compared to a reference healthy control data set (matched by gender, age, and body mass index) both spatiotemporal and gait control variables were different between groups, with walking speed, step time, and SDa (gait control parameter) demonstrating good discriminatory power (receiver operating characteristic area under the curve >0.8). These results provide a proof of principle of this relatively simple approach which, when applied in clinical practice, can detect a signal from those with known diabetes peripheral neuropathy. The technology has the potential to be used both routinely in the clinic and for tele-health applications. ...
Medicina
Background and Objectives: Diabetes mellitus is considered a serious public health problem due to its high prevalence and related complications, including gait and posture impairments due to neuropathy and vascular alterations and the subsequent increased risk of falls. The gait of patients with diabetes is characterized by alterations of the main spatiotemporal gait parameters such as gait velocity, cadence, stride time and length, which are also known to worsen with disease course. Wearable sensor systems can be used for gait analysis by providing spatiotemporal parameters and postural control (evaluated from the perspective of body sway), useful for investigating the disease progression. Thanks to their small size and low cost of their components, inertial measurement units (IMUs) are easy to wear and are cheap tools for movement analysis. Materials and Methods: The aim of this study is to review articles published in the last 21 years (from 2000 to 2021) concerning the applicati...
Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review
Electronics
Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further...
Biosensors, 2016
Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classifi...
IEEE Access, 2022
The mild symptoms in Mild Cognitive Impairment (MCI), a precursor of dementia, often go unnoticed and are assumed as normal aging signs. Such negligence result in late visits which consequently, lead to the diagnosis and progression of dementia. An instrumented gait assessment in home settings may facilitate the detection of subtle MCI-related motor deficits thus, allowing early diagnosis and intervention. This paper investigates potential gait biomarkers derived from shank mounted inertial sensors signals under normal and dual-task walking conditions using data collected from thirty MCI and thirty cognitively normal (CN) subjects. To identify potential gait biomarkers for MCI screening, we assess the variance and predictive power of each feature. Moreover, multiple classification models using different machine learning and feature selection techniques are built to automate MCI detection by leveraging the gait biomarkers. Statistical analysis reveal multiple gait parameters that are significantly different under both single and dualtask settings. However, we show that dual-task walking provides better distinction between MCI and CN subjects. The machine learning model employed for MCI pre-screening based on the inertial sensor-derived gait biomarkers achieves accuracy and sensitivity of 71.67% and 83.33%, respectively. INDEX TERMS Mild cognitive impairment, dementia, gait analysis, inertial sensors, gait biomarkers, early detection. AHSAN SHAHZAD (Member, IEEE) received the B.Sc. degree (Hons.
A multi-sensor wearable system for the assessment of diseased gait in real-world conditions
Frontiers in Bioengineering and Biotechnology
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, multipl...
Accelerometer-based gait assessment: Pragmatic deployment on an international scale
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016
Gait is emerging as a powerful tool to detect early disease and monitor progression across a number of pathologies. Typically quantitative gait assessment has been limited to specialised laboratory facilities. However, measuring gait in home and community settings may provide a more accurate reflection of gait performance because: (1) it will not be confounded by attention which may be heightened during formal testing; and it allows performance to be captured over time. This work addresses the feasibility and challenges of measuring gait characteristics with a single accelerometer based wearable device during free-living activity. Moreover, it describes the current methodological and statistical processes required to quantify those sensitive surrogate markers for ageing and pathology. A unified framework for large scale analysis is proposed. We present data and workflows from healthy older adults and those with Parkinson's disease (PD) while presenting current algorithms and scope within modern pervasive healthcare. Our findings suggested that free-living conditions heighten between group differences showing greater sensitivity to PD, and provided encouraging results to support the use of the suggested framework for large clinical application.
A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument
Diagnostics
Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, d...
Sensors, 2016
Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits. Specifically, the presented methodology was tested on gait data recorded on two pathological populations (Huntington's disease and post-stroke subjects) and healthy elderly controls using data from inertial measurement units placed at shank and waist. By extracting features from group-specific Hidden Markov Models (HMMs) and signal information in time and frequency domain, a Support Vector Machines classifier (SVM) was designed and validated. The 90.5% of subjects was assigned to the right group after leave-one-subject-out cross validation and majority voting. The long-term goal we point to is the gait assessment in everyday life to early detect gait alterations.
2022
Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term complication in diabetic patients associated with painful neuropathy, foot ulceration and amputation. The Michigan neuropathy screening instrument (MNSI) is one of the most common screening techniques for DSPN, however, it does not provide any direct severity grading system. Method: For designing and modelling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. MNSI variables and patient outcomes were investigated using machine learning tools to identify the features having higher association in DSPN identification. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading. Results: The top-7 ranked features from MNSI: 10-gm filament, Vibration perception (R), Vibration perception (L), previous diabetic neuropathy, the appearance of deformities, appearance of callus and appearance of fissure were identified as key features for identifying DSPN using the extra tree model. The area under the curve (AUC) of the nomogram for the internal and external datasets were 0.9421 and 0.946, respectively. From the developed nomogram, the probability of having DSPN was predicted and a DSPN severity scoring system for MNSI was developed from the probability score. The model performance was validated on an independent dataset. Patients were stratified into four severity levels: absent, mild, moderate, and severe using a cut-off value of 10.5, 12.7 and 15 for a DSPN probability less than 50%, 75% to 90%, and above 90%, respectively. Conclusions: This study provides a simple, easy-to-use and reliable algorithm for defining the prognosis and management of patients with DSPN.