Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases

Wearable sensors may enable the continuous monitoring of gait out of the clinic without requiring supervised tests and costly equipment. This paper investigates the use of a single wearable accelerometer to detect foot contact times and estimate temporal gait parameters (stride time, swing and stance duration). The experiments considered two possible body positions for the accelerometer: over the lower trunk and inside a trouser pocket. The latter approach could be implemented using a common smartphone. Notably, during the experiments, the ground truth was obtained by using a pair of sensorized shoes. Unlike ambient sensors and camera-based systems, sensorized shoes enable the evaluation of body-worn sensors even during longer walks. Experiments showed that both trunk and pocket positions achieved promising results in estimating gait parameters, with a mean absolute error below 50 ms.

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A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram

Journal of Healthcare Engineering ◽

10.1155/2018/7804243 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 15

Author(s):

Ludi Wang ◽

Wei Zhou ◽

Ying Xing ◽

Xiaoguang Zhou

Keyword(s):

Neural Network ◽

Blood Pressure ◽

Mean Absolute Error ◽

Wearable Sensors ◽

Absolute Error ◽

Noninvasive Estimation ◽

Treatment Of Hypertension ◽

The Mean ◽

Artificial Neural Network Ann ◽

Blood Pressure Estimation

The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

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The application of extended modified Lambert Beer model for measurement of blood carboxyhemoglobin and oxyhemoglobin saturation

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545814500266 ◽

2014 ◽

Vol 07 (03) ◽

pp. 1450026 ◽

Cited By ~ 10

Author(s):

Audrey Huong ◽

Xavier Ngu

Keyword(s):

Carbon Monoxide ◽

Continuous Monitoring ◽

Mean Absolute Error ◽

Absolute Error ◽

Co Poisoning ◽

Skin Model ◽

Oxyhemoglobin Saturation ◽

Fitting Procedure ◽

Alternative Means ◽

Absorption Characteristics

This work presents the use of extended Modified Lambert Beer (MLB) model for accurate and continuous monitoring of percent blood carboxyhemoglobin (COHb) (SCO) and oxyhemoglobin (OxyHb) saturation ( SO 2) via a fitting procedure. This quantification technique is based on the absorption characteristics of hemoglobin derivatives in the wavelength range of 520–600 nm to give the best estimates of the required parameters. A comparison of the performance of the developed model and MLB law is made using attenuation data from Monte Carlo simulations for a two-layered skin model. The results revealed a lower mean absolute error of 0.4% in the values estimated by the developed model as compared to 10% that is given by the MLB law. This study showed that the discussed approach is able to provide consistent and accurate measurement of blood SO 2 and SCO across different skin pigmentations suggesting that it may potentially be used as an alternative means for clinical diagnosis of carbon monoxide ( CO ) poisoning.

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Two-dimensional video-based analysis of human gait using pose estimation

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008935 ◽

2021 ◽

Vol 17 (4) ◽

pp. e1008935

Author(s):

Jan Stenum ◽

Cristina Rossi ◽

Ryan T. Roemmich

Keyword(s):

Gait Analysis ◽

Motion Capture ◽

Pose Estimation ◽

Mean Absolute Error ◽

Three Dimensional ◽

Absolute Error ◽

Human Gait ◽

Two Dimensional ◽

Gait Parameters ◽

Human Gait Analysis

Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s−1. Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for human gait analysis.

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Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

10.21203/rs.3.rs-33024/v1 ◽

2020 ◽

Author(s):

Andrius Apsega ◽

Liudvikas Petrauskas ◽

Vidmantas Alekna ◽

Kristina Daunoraviciene ◽

Viktorija Sevcenko ◽

...

Keyword(s):

Gait Analysis ◽

Gait Speed ◽

Stance Phase ◽

Wearable Sensors ◽

Logistic Models ◽

Stride Time ◽

Double Support ◽

Phase Time ◽

Gait Assessment ◽

Gait Parameters

Abstract Background: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail).Methods: 133 participants (≥ 60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters was assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index.Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19s), stance phase time (0.68s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity.Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.

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Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

Applied Sciences ◽

10.3390/app10238451 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8451

Author(s):

Andrius Apsega ◽

Liudvikas Petrauskas ◽

Vidmantas Alekna ◽

Kristina Daunoraviciene ◽

Viktorija Sevcenko ◽

...

Keyword(s):

Gait Analysis ◽

Sensitivity And Specificity ◽

Gait Speed ◽

Stance Phase ◽

Wearable Sensors ◽

Stride Time ◽

Double Support ◽

Phase Time ◽

Gait Assessment ◽

Gait Parameters

Background and objectives: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail). Materials and methods: 133 participants (≥60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters were assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19 s), stance phase time (0.68 s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity. Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.

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Inertial Measurement Unit-Based Estimation of Foot Trajectory for Clinical Gait Analysis

10.1101/595496 ◽

2019 ◽

Author(s):

Yumi Ono ◽

Koyu Hori ◽

Hiroki Ora ◽

Yuki Hirobe ◽

Yufeng Mao ◽

...

Keyword(s):

Gait Analysis ◽

Motion Capture ◽

Stride Length ◽

Wearable Sensors ◽

Analysis Method ◽

Inertial Measurement ◽

Gait Parameters ◽

Stride Duration ◽

Stance Duration ◽

Foot Trajectory

AbstractGait analysis is used widely in clinical practice for the evaluation of abnormal gait caused by disease. Conventionally, medical professionals use motion capture systems or make visual observations to evaluate a patient’s gait. Recent biomedical engineering studies have proposed easy-to-use gait analysis methods involving wearable sensors with inertial measurement units (IMUs). IMUs placed on the shanks just above the ankles allow for the long-term monitoring of gait because the participant can walk with or without shoes during the analysis. As far as the authors know, there is no report of the gait analysis method that estimates stride length, gait speed, stride duration, stance duration, and swing duration at the same time. In this study, we tested a proposed gait analysis method that uses IMUs attached on the shanks to estimate foot trajectory and temporal gait parameters. We evaluated this proposed method by analyzing the gait of 10 able-bodied participants (mean age 23.1 years, nine men and one woman). Wearable sensors were attached to the participants’ shanks, and we measured three-axis acceleration and three-axis angular velocity with the sensors to estimate foot trajectory during walking. We compared gait parameters estimated from the foot trajectory obtained with the proposed method and those measured with a motion capture system. Mean accuracy (mean ± standard deviation) was –0.046 ± 0.026 m for stride length, –0.036 ± 0.026 m/s for gait speed, –0.002 ± 0.019 s for stride duration, –0.000 ± 0.016 s for stance duration, and –0.002 ± 0.022 s for swing duration. These results suggest that the proposed method is useful for evaluation of clinical gait parameters.

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Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

10.26434/chemrxiv.13047863 ◽

2020 ◽

Author(s):

Jingbai Li ◽

Patrick Reiser ◽

André Eberhard ◽

Pascal Friederich ◽

Steven Lopez

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Excited State ◽

Adaptive Sampling ◽

Computational Cost ◽

Ground Truth ◽

Absolute Error ◽

Photochemical Reactions ◽

Computational Techniques ◽

Full Potential

Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr et al. demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH2NH2+).We have developed a Python-based code, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD), to accomplish the unprecedented 10 ns cis-trans photodynamics of trans-hexafluoro-2-butene (CF3–CH=CH–CF3) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S1 half-life (60.5 fs), the photochemical product ratio (trans: cis = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (trans → cis but not cis → trans pathways) that may offer future automated photochemical reaction discoveries.

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Contactless Core-Temperature Monitoring by Infrared Thermal Sensor using Mean Absolute Error Analysis

Recent Patents on Engineering ◽

10.2174/1872212115666201230091420 ◽

2020 ◽

Vol 15 ◽

Author(s):

Fahad Layth Malallah ◽

Baraa T. Shareef ◽

Mustafah Ghanem Saeed ◽

Khaled N. Yasen

Keyword(s):

Body Temperature ◽

Embedded System ◽

Core Temperature ◽

Integrated Circuit ◽

Mean Absolute Error ◽

Absolute Error ◽

Thermal Sensor ◽

Human Face ◽

Body Contact ◽

Arduino Microcontroller

Aims: Normally, the temperature increase of individuals leads to the possibility of getting a type of disease, which might be risky to other people such as coronavirus. Traditional techniques for tracking core-temperature require body contact either by oral, rectum, axillary, or tympanic, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing level of medical sectors is a necessary targets for the research operations, especially with the development of the integrated circuit, sensors and cameras that made the normal life easier. Methods: The solution is by proposing an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the real body temperature. Results: The Arduino is connected to an Infrared-Thermal sensor named MLX90614 as input signal, and connected to the LCD to display the CCT. To evaluate the proposed system, experiments are conducted by participating 31-subject sensing contactless temperature from the three face sub-regions: forehead, nose, and cheek. Conclusion: Experimental results approved that CCT can be measured remotely depending on the human face, in which the forehead region is better to be dependent, rather than nose and cheek regions for CCT measurement due to the smallest

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Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽

10.3390/s21082670 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2670

Author(s):

Thomas Quirin ◽

Corentin Féry ◽

Dorian Vogel ◽

Céline Vergne ◽

Mathieu Sarracanie ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Brain Stimulation ◽

Mean Absolute Error ◽

Tracking System ◽

Three Dimensional ◽

Absolute Error ◽

Magnetic Tracking ◽

Magnetic Camera ◽

Magnetic Resonance Imaging Mri ◽

Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

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A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽

10.3390/s21113719 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3719

Author(s):

Aoxin Ni ◽

Arian Azarang ◽

Nasser Kehtarnavaz

Keyword(s):

Heart Rate ◽

Deep Learning ◽

Mean Absolute Error ◽

Absolute Error ◽

Video Camera ◽

Measurement Methods ◽

Rate Measurement ◽

Learning Methods ◽

The Public ◽

Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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