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22 pages, 3224 KiB

Open AccessArticle

Monitoring of Gait Parameters in Post-Stroke Individuals: A Feasibility Study Using RGB-D Sensors

by Claudia Ferraris, Veronica Cimolin, Luca Vismara, Valerio Votta, Gianluca Amprimo, Riccardo Cremascoli, Manuela Galli, Roberto Nerino, Alessandro Mauro and Lorenzo Priano

Sensors 2021, 21(17), 5945; https://doi.org/10.3390/s21175945 - 04 Sep 2021

Cited by 20 | Viewed by 3552

Abstract

Stroke is one of the most significant causes of permanent functional impairment and severe motor disability. Hemiplegia or hemiparesis are common consequences of the acute event, which negatively impacts daily life and requires continuous rehabilitation treatments to favor partial or complete recovery and, [...] Read more.

Stroke is one of the most significant causes of permanent functional impairment and severe motor disability. Hemiplegia or hemiparesis are common consequences of the acute event, which negatively impacts daily life and requires continuous rehabilitation treatments to favor partial or complete recovery and, consequently, to regain autonomy, independence, and safety in daily activities. Gait impairments are frequent in stroke survivors. The accurate assessment of gait anomalies is therefore crucial and a major focus of neurorehabilitation programs to prevent falls or injuries. This study aims to estimate, using a single RGB-D sensor, gait patterns and parameters on a short walkway. This solution may be suitable for monitoring the improvement or worsening of gait disorders, including in domestic and unsupervised scenarios. For this purpose, some of the most relevant spatiotemporal parameters, estimated by the proposed solution on a cohort of post-stroke individuals, were compared with those estimated by a gold standard system for a simultaneous instrumented 3D gait analysis. Preliminary results indicate good agreement, accuracy, and correlation between the gait parameters estimated by the two systems. This suggests that the proposed solution may be employed as an intermediate tool for gait analysis in environments where gold standard systems are impractical, such as home and ecological settings in real-life contexts. Full article

(This article belongs to the Special Issue Sensors for Gait, Posture and Health Monitoring)

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11 pages, 1926 KiB

Open AccessArticle

Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task

by Cameron Diep, Johanna O’Day, Yasmine Kehnemouyi, Gary Burnett and Helen Bronte-Stewart

Sensors 2021, 21(8), 2661; https://doi.org/10.3390/s21082661 - 10 Apr 2021

Cited by 7 | Viewed by 3927

Abstract

Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires [...] Read more.

Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring the center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (area under the curve = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD. Full article

(This article belongs to the Special Issue Sensors for Gait, Posture and Health Monitoring)

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19 pages, 16796 KiB

Open AccessArticle

A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach

by Po-Yuan Jeng, Li-Chun Wang, Chaur-Jong Hu and Dean Wu

Sensors 2021, 21(1), 258; https://doi.org/10.3390/s21010258 - 02 Jan 2021

Cited by 16 | Viewed by 3756

Abstract

In the hospital, a sleep postures monitoring system is usually adopted to transform sensing signals into sleep behaviors. However, a home-care sleep posture monitoring system needs to be user friendly. In this paper, we present iSleePost—a user-friendly home-care intelligent sleep posture monitoring system. [...] Read more.

In the hospital, a sleep postures monitoring system is usually adopted to transform sensing signals into sleep behaviors. However, a home-care sleep posture monitoring system needs to be user friendly. In this paper, we present iSleePost—a user-friendly home-care intelligent sleep posture monitoring system. We address the labor-intensive labeling issue of traditional machine learning approaches in the training phase. Our proposed mobile health (mHealth) system leverages the communications and computation capabilities of mobile phones for provisioning a continuous sleep posture monitoring service. Our experiments show that iSleePost can achieve up to 85 percent accuracy in recognizing sleep postures. More importantly, iSleePost demonstrates that an easy-to-wear wrist sensor can accurately quantify sleep postures after our designed training phase. It is our hope that the design concept of iSleePost can shed some lights on quantifying human sleep postures in the future. Full article

(This article belongs to the Special Issue Sensors for Gait, Posture and Health Monitoring)

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21 pages, 1986 KiB

Open AccessArticle

Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning

by Ivana Kiprijanovska, Hristijan Gjoreski and Matjaž Gams

Sensors 2020, 20(18), 5373; https://doi.org/10.3390/s20185373 - 19 Sep 2020

Cited by 34 | Viewed by 5057

Abstract

Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are [...] Read more.

Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist. Full article

(This article belongs to the Special Issue Sensors for Gait, Posture and Health Monitoring)

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16 pages, 875 KiB

Open AccessArticle

Instrumented Four Square Step Test in Adults with Transfemoral Amputation: Test-Retest Reliability and Discriminant Validity between Two Types of Microprocessor Knees

by Arnaud Gouelle and Michael Jason Highsmith

Sensors 2020, 20(17), 4782; https://doi.org/10.3390/s20174782 - 24 Aug 2020

Cited by 3 | Viewed by 4847

Abstract

Technology-based outcomes have recently been proposed to complement the standard Four Square Step Test (FSST) by providing a decomposition of the sequences and information about the stepping pattern. A test-retest study and a randomized crossover design have been used to determine immediate test-retest [...] Read more.

Technology-based outcomes have recently been proposed to complement the standard Four Square Step Test (FSST) by providing a decomposition of the sequences and information about the stepping pattern. A test-retest study and a randomized crossover design have been used to determine immediate test-retest reliability and to assess discriminant validity, in persons with a unilateral transfemoral amputation, for the parameters computed by an instrumented version of the Four Square Step Test. Twenty adults, independent and unlimited community ambulators, with a unilateral transfemoral amputation, performed two Four Square Step Tests on a pressure mat first with a microprocessor knee, then, a few weeks later with another one. One of these prosthetic knees was acknowledged to be superior and to provide functional improvement. Test-retest, intraclass correlation coefficients and minimal detectable change at 95% confidence level were calculated for each variable. Paired samples t-tests were then used to identify differences between the two microprocessor knee systems. The test-retest reliability of most outcome measures was good to excellent. Few variables showed a systematic difference and a trend to improve between test 1 and test 2. When comparing both microprocessor knees, significant differences in the expected direction were observed, with interpretation in accordance with a functional improvement. Importantly, we highlighted that various strategies to improve the performance in the test might complexify the interpretation of the most detailed measurement. The instrumented Four Square Step test provides reliable measures with satisfactory test-retest reliability and discriminant validity in persons with unilateral transfemoral amputation. Full article

(This article belongs to the Special Issue Sensors for Gait, Posture and Health Monitoring)

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20 pages, 1136 KiB

Open AccessArticle

Body Mass Index in Human Gait for Building Risk Assessment Using Graph Theory

by Washington Velásquez, Manuel S. Alvarez-Alvarado, Andres Munoz-Arcentales, Sonsoles López-Pernas and Joaquín Salvachúa

Sensors 2020, 20(10), 2899; https://doi.org/10.3390/s20102899 - 20 May 2020

Cited by 3 | Viewed by 2410

Abstract

This article presents a comprehensive study of human physiology to determine the impact of body mass index (BMI) on human gait. The approach followed in this study consists of a mathematical model based on the centre of mass of the human body, the [...] Read more.

This article presents a comprehensive study of human physiology to determine the impact of body mass index (BMI) on human gait. The approach followed in this study consists of a mathematical model based on the centre of mass of the human body, the inertia of a person in motion and the human gait speed. Moreover, the study includes the representation of a building using graph theory and emulates the presence of a person inside the building when an emergency takes place. The optimal evacuation route is obtained using the breadth-first search (BFS) algorithm, and the evacuation time prediction is calculated using a Gaussian process model. Then, the risk of the building is quantified by using a non-sequential Monte Carlo simulation. The results open up a new horizon for developing a more realistic model for the assessment of civil safety. Full article

(This article belongs to the Special Issue Sensors for Gait, Posture and Health Monitoring)

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15 pages, 6221 KiB

Open AccessArticle

A New Automatic Foot Arch Index Measurement Method Based on a Flexible Membrane Pressure Sensor

by Tao Zheng, Zhiyong Yu, Jin Wang and Guodong Lu

Sensors 2020, 20(10), 2892; https://doi.org/10.3390/s20102892 - 20 May 2020

Cited by 5 | Viewed by 5100

Abstract

The foot arch index is an important index to evaluate the health of human feet and the biomechanics line, aiming at addressing the shortcomings of the low efficiency and slow speed of manual foot arch index measurement; in this work, an automatic foot [...] Read more.

The foot arch index is an important index to evaluate the health of human feet and the biomechanics line, aiming at addressing the shortcomings of the low efficiency and slow speed of manual foot arch index measurement; in this work, an automatic foot arch index measurement method based on a flexible membrane pressure sensor was proposed. The distribution of plantar pressure data was obtained from the flexible membrane pressure sensor and converted into a digital image. The 8-neighborhood correlation pixel method was proposed to remove the interference of isolated noise points. In order to remove the toes’ data without affecting the foot sole data, the row element association algorithm was proposed. The front and back endpoints of the foot were automatically located to obtain the foot length, and the foot arch index was also automatically obtained based on the foot arch pressure area. Whether it was a high arch foot, flat foot or normal foot, the method proposed in this paper could accurately and quickly distinguish them. The prototype was developed, and its feasibility and validity were verified by a series of tests. Full article

(This article belongs to the Special Issue Sensors for Gait, Posture and Health Monitoring)

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Review

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30 pages, 6009 KiB

Open AccessReview

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

by Szymon Sieciński, Paweł S. Kostka and Ewaryst J. Tkacz

Sensors 2020, 20(22), 6675; https://doi.org/10.3390/s20226675 - 22 Nov 2020

Cited by 34 | Viewed by 3900

Abstract

Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (microelectromechanical system) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight [...] Read more.

Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (microelectromechanical system) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight into the mechanical aspects of the cardiac cycle. In this review, we describe the summary of the history, definition, measurements, waveform description and applications of gyrocardiography. The review was conducted on about 55 works analyzed between November 2016 and September 2020. The aim of this literature review was to summarize the current state of knowledge in gyrocardiography, especially the definition, waveform description, the physiological and physical sources of the signal and its applications. Based on the analyzed works, we present the definition of GCG as a technique for registration and analysis of rotational component of local cardiac vibrations, waveform annotation, several applications of the gyrocardiography, including, heart rate estimation, heart rate variability analysis, hemodynamics analysis, and classification of various cardiac diseases. Full article

(This article belongs to the Special Issue Sensors for Gait, Posture and Health Monitoring)

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