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18 pages, 7145 KiB

Open AccessArticle

Clinical Evaluation in Parkinson’s Disease: Is the Golden Standard Shiny Enough?

by Foivos S. Kanellos, Konstantinos I. Tsamis, Georgios Rigas, Yannis V. Simos, Andreas P. Katsenos, Gerasimos Kartsakalis, Dimitrios I. Fotiadis, Patra Vezyraki, Dimitrios Peschos and Spyridon Konitsiotis

Sensors 2023, 23(8), 3807; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083807 - 07 Apr 2023

Cited by 3 | Viewed by 1970

Abstract

Parkinson’s disease (PD) has become the second most common neurodegenerative condition following Alzheimer’s disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, [...] Read more.

Parkinson’s disease (PD) has become the second most common neurodegenerative condition following Alzheimer’s disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, expert neurologists evaluate disease progression using established rating scales and patient-reported questionnaires, which have interpretability issues and are subject to recall bias. In this context, artificial-intelligence-driven telehealth solutions, such as wearable devices, have the potential to improve patient care and support physicians to manage PD more effectively by monitoring patients in their familiar environment in an objective manner. In this study, we evaluate the validity of in-office clinical assessment using the MDS-UPDRS rating scale compared to home monitoring. Elaborating the results for 20 patients with Parkinson’s disease, we observed moderate to strong correlations for most symptoms (bradykinesia, rest tremor, gait impairment, and freezing of gait), as well as for fluctuating conditions (dyskinesia and OFF). In addition, we identified for the first time the existence of an index capable of remotely measuring patients’ quality of life. In summary, an in-office examination is only partially representative of most PD symptoms and cannot accurately capture daytime fluctuations and patients’ quality of life. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled System for Health and Biomechanical Monitoring)

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19 pages, 1699 KiB

Open AccessArticle

Influence of the Backward Fall Technique on the Transverse Linear Acceleration of the Head during the Fall

by Andrzej Mroczkowski and Redha Taiar

Sensors 2023, 23(6), 3276; https://0-doi-org.brum.beds.ac.uk/10.3390/s23063276 - 20 Mar 2023

Viewed by 1404

Abstract

Background: The formation of large accelerations on the head and cervical spine during a backward fall is particularly dangerous due to the possibility of affecting the central nervous system (CNS). It may eventually lead to serious injuries and even death. This research aimed [...] Read more.

Background: The formation of large accelerations on the head and cervical spine during a backward fall is particularly dangerous due to the possibility of affecting the central nervous system (CNS). It may eventually lead to serious injuries and even death. This research aimed to determine the effect of the backward fall technique on the linear acceleration of the head in the transverse plane in students practicing various sports disciplines. Methods: The study involved 41 students divided into two study groups. Group A consisted of 19 martial arts practitioners who, during the study, performed falls using the side aligning of the body technique. Group B consisted of 22 handball players who, during the study, performed falls using the technique performed in a way similar to a gymnastic backward roll. A rotating training simulator (RTS) was used to force falls, and a Wiva^® Science apparatus was used to assess acceleration. Results: The greatest differences in backward fall acceleration were found between the groups during the buttocks’ contact with the ground. Larger changes in head acceleration were noted in group B. Conclusions: The lower changes in head acceleration obtained in physical education students falling with a lateral body position compared to students training handball indicate their lower susceptibility to head, cervical spine, and pelvis injuries when falling backwards as caused by horizontal force. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled System for Health and Biomechanical Monitoring)

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12 pages, 2510 KiB

Open AccessArticle

Development of a Biomechanical Device for Parameter Quantification Associated with the Sit-to-Stand Movement

by Natacha Oliveira, Filipe Carvalho, Jorge Laíns, Deolinda Rasteiro and Luis Roseiro

Sensors 2023, 23(4), 1837; https://0-doi-org.brum.beds.ac.uk/10.3390/s23041837 - 07 Feb 2023

Cited by 1 | Viewed by 1122

Abstract

The “sit-to-stand” (STS) movement is essential during activities of daily living (ADL). In individuals with physical-motor diseases, its execution and repetition increases activity levels, which is crucial for a good motor rehabilitation process and daily training. Interestingly, there are no sit-to-stand devices that [...] Read more.

The “sit-to-stand” (STS) movement is essential during activities of daily living (ADL). In individuals with physical-motor diseases, its execution and repetition increases activity levels, which is crucial for a good motor rehabilitation process and daily training. Interestingly, there are no sit-to-stand devices that allow a quantitative assessment of the key variables that happen during STS, and there is a need to come up with a new device. This work presents a developed biomechanical support device that measures the force of the upper limbs during the STS movement, aiming to motivate and encourage people undergoing physical therapy in the lower limbs. The device uses two instrumented beams and allows real-time visualization of both arms’ applied force and it records the data for post-processing. The device was tested with a well-defined protocol on a group of 34 healthy young volunteers and an elderly group of 16 volunteers from a continuing care unit. The system showed robust strength and stiffness, good usability, and a user interface that acquired and recorded data effectively, allowing one to observe force-time during the execution of the movement through the application interface developed and in recording data for post-processing. Asymmetries in the applied forces in the STS movement between the upper limbs were identified, particularly in volunteers of the continuing care unit. From the application and the registered data, it can be observed that volunteers with motor problems in the lower limbs performed more strength in their arms to compensate. As expected, the maximum average strength of the healthy volunteers (both arms: force = 105 Newton) was higher than that of the volunteers from the continuing care unit (right arm: force = 54 Newton; left arm: force = 56 Newton). Among others, moderate correlations were observed between weight-applied and height-applied forces and there was a moderately high correlation between the Sequential Clinical Assessment of Respiratory Function (SCAR-F score) and time to perform the movement. Based on the obtained results, the developed device can be a helpful tool for monitoring the evaluation of a patient with limitations in the upper and lower limbs. In addition, the developed system allows for easy evolution, such as including a barometric platform and implementing serious games that can stimulate the execution of the STS movement. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled System for Health and Biomechanical Monitoring)

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12 pages, 8834 KiB

Open AccessArticle

Predicting Wrist Posture during Occupational Tasks Using Inertial Sensors and Convolutional Neural Networks

by Calvin Young, Andrew Hamilton-Wright, Michele L. Oliver and Karen D. Gordon

Sensors 2023, 23(2), 942; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020942 - 13 Jan 2023

Cited by 1 | Viewed by 1430

Abstract

Current methods for ergonomic assessment often use video-analysis to estimate wrist postures during occupational tasks. Wearable sensing and machine learning have the potential to automate this tedious task, and in doing so greatly extend the amount of data available to clinicians and researchers. [...] Read more.

Current methods for ergonomic assessment often use video-analysis to estimate wrist postures during occupational tasks. Wearable sensing and machine learning have the potential to automate this tedious task, and in doing so greatly extend the amount of data available to clinicians and researchers. A method of predicting wrist posture from inertial measurement units placed on the wrist and hand via a deep convolutional neural network has been developed. This study has quantified the accuracy and reliability of the postures predicted by this system relative to the gold standard of optoelectronic motion capture. Ten participants performed 3 different simulated occupational tasks on 2 occasions while wearing inertial measurement units on the hand and wrist. Data from the occupational task recordings were used to train a convolutional neural network classifier to estimate wrist posture in flexion/extension, and radial/ulnar deviation. The model was trained and tested in a leave-one-out cross validation format. Agreement between the proposed system and optoelectronic motion capture was 65% with

κ

= 0.41 in flexion/extension and 60% with

κ

= 0.48 in radial/ulnar deviation. The proposed system can predict wrist posture in flexion/extension and radial/ulnar deviation with accuracy and reliability congruent with published values for human estimators. This system can estimate wrist posture during occupational tasks in a small fraction of the time it takes a human to perform the same task. This offers opportunity to expand the capabilities of practitioners by eliminating the tedium of manual postural assessment. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled System for Health and Biomechanical Monitoring)

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13 pages, 3832 KiB

Open AccessArticle

The Validity and Reliability of a New Intelligent Three-Dimensional Gait Analysis System in Healthy Subjects and Patients with Post-Stroke

by Yingpeng Wang, Ran Tang, Hujun Wang, Xin Yu, Yingqi Li, Congxiao Wang, Luyi Wang and Shuyan Qie

Sensors 2022, 22(23), 9425; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239425 - 02 Dec 2022

Cited by 2 | Viewed by 1616

Abstract

Odonate is a new, intelligent three-dimensional gait analysis system based on binocular depth cameras and neural networks, but its accuracy has not been validated. Twenty-six healthy subjects and sixteen patients with post-stroke were recruited to investigate the validity and reliability of Odonate for [...] Read more.

Odonate is a new, intelligent three-dimensional gait analysis system based on binocular depth cameras and neural networks, but its accuracy has not been validated. Twenty-six healthy subjects and sixteen patients with post-stroke were recruited to investigate the validity and reliability of Odonate for gait analysis and examine its ability to discriminate abnormal gait patterns. The repeatability tests of different raters and different days showed great consistency. Compared with the results measured by Vicon, gait velocity, cadence, step length, cycle time, and sagittal hip and knee joint angles measured by Odonate showed high consistency, while the consistency of the gait phase division and the sagittal ankle joint angle was slightly lower. In addition, the stages with statistical differences between healthy subjects and patients during a gait cycle measured by the two systems were consistent. In conclusion, Odonate has excellent inter/intra-rater reliability, and has strong validity in measuring some spatiotemporal parameters and the sagittal joint angles, except the gait phase division and the ankle joint angle. Odonate is comparable to Vicon in its ability to identify abnormal gait patterns in patients with post-stroke. Therefore, Odonate has the potential to provide accessible and objective measurements for clinical gait assessment. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled System for Health and Biomechanical Monitoring)

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18 pages, 6608 KiB

Open AccessArticle

Fatigue Effect on Minimal Toe Clearance and Toe Activity during Walking

by Yingjie Jin, Yui Sano, Miho Shogenji and Tetsuyou Watanabe

Sensors 2022, 22(23), 9300; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239300 - 29 Nov 2022

Cited by 1 | Viewed by 1314

Abstract

This study investigates the effects of fatigue on the process of walking in young adults using the developed clog-integrated sensor system. The developed sensor can simultaneously measure the forefoot activity (FA) and minimum toe clearance (MTC). The FA was evaluated through the change [...] Read more.

This study investigates the effects of fatigue on the process of walking in young adults using the developed clog-integrated sensor system. The developed sensor can simultaneously measure the forefoot activity (FA) and minimum toe clearance (MTC). The FA was evaluated through the change in the contact area captured by a camera using a method based on a light conductive plate. The MTC was derived from the distance between the bottom surface of the clog and ground obtained using a time of flight (TOF) sensor, and the clog posture was obtained using an acceleration sensor. The induced fatigue was achieved by walking on a treadmill at the fastest walking speed. We evaluated the FA and MTC before and after fatigue in both feet for 14 participants. The effects of fatigue manifested in either the FA or MTC of either foot when the results were evaluated by considering the participants individually, although individual variances in the effects of fatigue were observed. In the dominant foot, a significant increase in either the FA or MTC was observed in 13 of the 14 participants. The mean MTC in the dominant foot increased significantly (p = 0.038) when the results were evaluated by considering the participants as a group. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled System for Health and Biomechanical Monitoring)

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19 pages, 4927 KiB

Open AccessArticle

Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data

by Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Hafizur Rahman, Basheer Qolomany, Iraklis I. Pipinos, Fadi Alsaleem and Sara A. Myers

Sensors 2022, 22(19), 7432; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197432 - 30 Sep 2022

Cited by 5 | Viewed by 2650

Abstract

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of [...] Read more.

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, and hip joint angles, torques, and powers, as well as ground reaction forces (GRF). ML was able to classify those with and without PAD using Neural Networks or Random Forest algorithms with 89% accuracy (0.64 Matthew’s Correlation Coefficient) using all laboratory-based gait variables. Moreover, models using only GRF variables provided up to 87% accuracy (0.64 Matthew’s Correlation Coefficient). These results indicate that ML models can classify those with and without PAD using gait signatures with acceptable performance. Results also show that an ML gait signature model that uses GRF features delivers the most informative data for PAD classification. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled System for Health and Biomechanical Monitoring)

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17 pages, 5045 KiB

Open AccessArticle

Monitoring Breathing and Heart Rate Using Episodic Broadcast Data Transmission

by Paweł Janik, Małgorzata A. Janik and Michał Pielka

Sensors 2022, 22(16), 6019; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166019 - 12 Aug 2022

Cited by 2 | Viewed by 1739

Abstract

The paper presents a wearable sensor for breath and pulse monitoring using an inertial sensor and episodic broadcast radio transmission. The data transmission control algorithm applied allows for the transmission of additional information using the standard PDU format and, at the same time, [...] Read more.

The paper presents a wearable sensor for breath and pulse monitoring using an inertial sensor and episodic broadcast radio transmission. The data transmission control algorithm applied allows for the transmission of additional information using the standard PDU format and, at the same time, goes beyond the Bluetooth teletransmission standard (BLE). The episodic broadcast transmission makes it possible to receive information from sensors without the need to create a dedicated radio link or a defined network structure. The radio transmission controlled by the occurrence of a specific event in the monitored signal is combined with the reference wire transmission. The signals from two different types of sensors and the simulated ECG signal are used to control the BLE transmission. The presented results of laboratory tests indicate the effectiveness of episodic data transmission in the BLE standard. The conducted analysis showed that the mean difference in pulse detection using the episodic transmission compared to the wire transmission is 0.038 s, which is about 4% of the mean duration of a single cycle, assuming that the average adult human pulse is 60 BPM. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled System for Health and Biomechanical Monitoring)

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