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20 pages, 749 KiB

Open AccessArticle

The Stress of Measuring Plantar Tissue Stress in People with Diabetes-Related Foot Ulcers: Biomechanical and Feasibility Findings from Two Prospective Cohort Studies

by Chantal M. Hulshof, Madelyn Page, Sjef G. van Baal, Sicco A. Bus, Malindu E. Fernando, Lisette van Gemert-Pijnen, Kilian D. R. Kappert, Scott Lucadou-Wells, Bijan Najafi, Jaap J. van Netten and Peter A. Lazzarini

Sensors 2024, 24(8), 2411; https://0-doi-org.brum.beds.ac.uk/10.3390/s24082411 - 10 Apr 2024

Viewed by 1022

Abstract

Reducing high mechanical stress is imperative to heal diabetes-related foot ulcers. We explored the association of cumulative plantar tissue stress (CPTS) and plantar foot ulcer healing, and the feasibility of measuring CPTS, in two prospective cohort studies (Australia (AU) and The Netherlands (NL)). [...] Read more.

Reducing high mechanical stress is imperative to heal diabetes-related foot ulcers. We explored the association of cumulative plantar tissue stress (CPTS) and plantar foot ulcer healing, and the feasibility of measuring CPTS, in two prospective cohort studies (Australia (AU) and The Netherlands (NL)). Both studies used multiple sensors to measure factors to determine CPTS: plantar pressures, weight-bearing activities, and adherence to offloading treatments, with thermal stress response also measured to estimate shear stress in the AU-study. The primary outcome was ulcer healing at 12 weeks. Twenty-five participants were recruited: 13 in the AU-study and 12 in the NL-study. CPTS data were complete for five participants (38%) at baseline and one (8%) during follow-up in the AU-study, and one (8%) at baseline and zero (0%) during follow-up in the NL-study. Reasons for low completion at baseline were technical issues (AU-study: 31%, NL-study: 50%), non-adherent participants (15% and 8%) or combinations (15% and 33%); and at follow-up refusal of participants (62% and 25%). These underpowered findings showed that CPTS was non-significantly lower in people who healed compared with non-healed people (457 [117; 727], 679 [312; 1327] MPa·s/day). Current feasibility of CPTS seems low, given technical challenges and non-adherence, which may reflect the burden of treating diabetes-related foot ulcers. Full article

(This article belongs to the Special Issue Novel Sensing Technologies for Digital Health)

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14 pages, 1472 KiB

Open AccessArticle

Automatic Assessments of Parkinsonian Gait with Wearable Sensors for Human Assistive Systems

by Yi Han, Xiangzhi Liu, Ning Zhang, Xiufeng Zhang, Bin Zhang, Shuoyu Wang, Tao Liu and Jingang Yi

Sensors 2023, 23(4), 2104; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042104 - 13 Feb 2023

Cited by 3 | Viewed by 1782

Abstract

The rehabilitation evaluation of Parkinson’s disease has always been the research focus of human assistive systems. It is a research hotspot to objectively and accurately evaluate the gait condition of Parkinson’s disease patients, thereby adjusting the actuators of the human–machine system and making [...] Read more.

The rehabilitation evaluation of Parkinson’s disease has always been the research focus of human assistive systems. It is a research hotspot to objectively and accurately evaluate the gait condition of Parkinson’s disease patients, thereby adjusting the actuators of the human–machine system and making rehabilitation robots better adapt to the recovery process of patients. The rehabilitation evaluation of Parkinson’s disease has always been the research focus of rehabilitation robots. It is a research hotspot to be able to objectively and accurately evaluate the recovery of Parkinson’s disease patients, thereby adjusting the driving module of the human–machine collaboration system in real time, so that rehabilitation robots can better adapt to the recovery process of Parkinson’s disease. The gait task in the Unified Parkinson’s Disease Rating Scale (UPDRS) is a widely accepted standard for assessing the gait impairments of patients with Parkinson’s disease (PD). However, the assessments conducted by neurologists are always subjective and inaccurate, and the results are determined by the neurologists’ observation and clinical experience. Thus, in this study, we proposed a novel machine learning-based method of automatically assessing the gait task in UPDRS with wearable sensors as a more convenient and objective alternative means for PD gait assessment. In the design, twelve gait features, including three spatial–temporal features and nine kinematic features, were extracted and calculated from two shank-mounted IMUs. A novel nonlinear model is developed for calculating the score of gait task from the gait features. Twenty-five PD patients and twenty-eight healthy subjects were recruited for validating the proposed method. For comparison purpose, three traditional models, which have been used in previous studies, were also tested by the same dataset. In terms of percentages of participants, 84.9%, 73.6%, 73.6%, and 66.0% of the participants were accurately assigned into the true level with the proposed nonlinear model, the support vector machine model, the naive Bayes model, and the linear regression model, respectively, which indicates that the proposed method has a good performance on calculating the score of the UPDRS gait task and conformance with the rating done by neurologists. Full article

(This article belongs to the Special Issue Novel Sensing Technologies for Digital Health)

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18 pages, 5493 KiB

Open AccessArticle

Force-Moment Sensor for Prosthesis Structural Load Measurement

by Md Rejwanul Haque, Greg Berkeley and Xiangrong Shen

Sensors 2023, 23(2), 938; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020938 - 13 Jan 2023

Cited by 1 | Viewed by 1651

Abstract

Measurement of prosthesis structural load, as an important way to quantify the interaction of the amputee user with the environment, may serve important purposes in the control of smart lower-limb prosthetic devices. However, the majority of existing force sensors used in protheses are [...] Read more.

Measurement of prosthesis structural load, as an important way to quantify the interaction of the amputee user with the environment, may serve important purposes in the control of smart lower-limb prosthetic devices. However, the majority of existing force sensors used in protheses are developed based on strain measurement and thus may suffer from multiple issues such as weak signals and signal drifting. To address these limitations, this paper presents a novel Force-Moment Prosthesis Load Sensor (FM-PLS) to measure the axial force and bending moment in the structure of a lower-limb prosthesis. Unlike strain gauge-based force sensors, the FM-PLS is developed based on the magnetic sensing of small (millimeter-scale) deflection of an elastic element, and it may provide stronger signals that are more robust against interferences and drifting since such physical deflection is several orders of magnitude greater than the strain of a typical load-bearing structure. The design of the sensor incorporates uniquely curved supporting surfaces such that the measurement is sensitive to light load but the sensor structure is robust enough to withstand heavy load without damage. To validate the sensor performance, benchtop testing of the FM-PLS and walking experiments of a FM-PLS-embedded robotic lower-limb prosthesis were conducted. Benchtop testing results displayed good linearity and a good match to the numerical simulation results. Results from the prosthesis walking experiments showed that the sensor signals can be used to detect important gaits events such as heel strike and toe-off, facilitating the reliable motion control of lower-limb prostheses. Full article

(This article belongs to the Special Issue Novel Sensing Technologies for Digital Health)

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33 pages, 7019 KiB

Open AccessArticle

A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring

by Leonor Pereira and Hugo Plácido da Silva

Sensors 2023, 23(2), 719; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020719 - 08 Jan 2023

Cited by 7 | Viewed by 2803

Abstract

In recent years, employment in sedentary occupations has continuously risen. Office workers are more prone to prolonged static sitting, spending 65–80% of work hours sitting, increasing risks for multiple health problems, including cardiovascular diseases and musculoskeletal disorders. These adverse health effects lead to [...] Read more.

In recent years, employment in sedentary occupations has continuously risen. Office workers are more prone to prolonged static sitting, spending 65–80% of work hours sitting, increasing risks for multiple health problems, including cardiovascular diseases and musculoskeletal disorders. These adverse health effects lead to decreased productivity, increased absenteeism and health care costs. However, lack of regulation targeting these issues has oftentimes left them unattended. This article proposes a smart chair system, with posture and electrocardiography (ECG) monitoring modules, using an “invisible” sensing approach, to optimize working conditions, without hindering everyday tasks. For posture classification, machine learning models were trained and tested with datasets composed by center of mass coordinates in the seat plane, computed from the weight measured by load cells fixed under the seat. Models were trained and evaluated in the classification of five and seven sitting positions, achieving high accuracy results for all five-class models (>97.4%), and good results for some seven-class models, particularly the best performing k-NN model (87.5%). For ECG monitoring, signals were acquired at the armrests covered with conductive nappa, connected to a single-lead sensor. Following signal filtering and segmentation, several outlier detection methods were applied to remove extremely noisy segments with mislabeled R-peaks, but only DBSCAN showed satisfactory results for the ECG segmentation performance (88.21%) and accuracy (90.50%). Full article

(This article belongs to the Special Issue Novel Sensing Technologies for Digital Health)

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19 pages, 4372 KiB

Open AccessArticle

A Mobile Application to Perform the Six-Minute Walk Test (6MWT) at Home: A Random Walk in the Park Is as Accurate as a Standardized 6MWT

by Martijn Scherrenberg, Cindel Bonneux, Deeman Yousif Mahmood, Dominique Hansen, Paul Dendale and Karin Coninx

Sensors 2022, 22(11), 4277; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114277 - 03 Jun 2022

Cited by 7 | Viewed by 2211

Abstract

The six-minute walk test (6MWT) provides an objective measurement of a person’s functional exercise capacity. In this study, we developed a smartphone application that allows cardiac patients to do a self-administered 6MWT at home on a random trajectory. In a prospective study with [...] Read more.

The six-minute walk test (6MWT) provides an objective measurement of a person’s functional exercise capacity. In this study, we developed a smartphone application that allows cardiac patients to do a self-administered 6MWT at home on a random trajectory. In a prospective study with 102 cardiovascular disease patients, we aimed to identify the optimal circumstances to perform a smartphone-measured 6MWT, i.e., the best algorithm and the best position to wear the smartphone during the test. Furthermore, we investigated if a random walk is as accurate as a standardized 6MWT. When considering both the reliability and accuracy of the distance walked, the best circumstances to perform a standardized smartphone-measured 6MWT are wearing the smartphone in a strap around the patient’s arm and using an algorithm that relies on the processed step count data acquired from Google Fit. Furthermore, we demonstrated that a smartphone-measured walk along a random trajectory is as accurate to determine a cardiac patient’s functional exercise capacity as a standardized (smartphone-measured) 6MWT. We conclude this paper by presenting how our 6MWT application can be used in a home setting to remotely follow up on cardiac patients’ functional exercise capacity. Full article

(This article belongs to the Special Issue Novel Sensing Technologies for Digital Health)

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16 pages, 17320 KiB

Open AccessArticle

Silicone-Textile Composite Resistive Strain Sensors for Human Motion-Related Parameters

by Joshua Di Tocco, Daniela Lo Presti, Alberto Rainer, Emiliano Schena and Carlo Massaroni

Sensors 2022, 22(10), 3954; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103954 - 23 May 2022

Cited by 9 | Viewed by 1962

Abstract

In recent years, soft and flexible strain sensors have found application in wearable devices for monitoring human motion and physiological parameters. Conductive textile-based sensors are good candidates for developing these sensors. However, their robust electro-mechanical connection and susceptibility to environmental factors are still [...] Read more.

In recent years, soft and flexible strain sensors have found application in wearable devices for monitoring human motion and physiological parameters. Conductive textile-based sensors are good candidates for developing these sensors. However, their robust electro-mechanical connection and susceptibility to environmental factors are still an open challenge to date. In this work, the manufacturing process of a silicone-textile composite resistive strain sensor based on a conductive resistive textile encapsulated into a dual-layer of silicone rubber is reported. In the working range typical of biomedical applications (up to 50%), the proposed flexible, skin-safe and moisture resistant strain sensor exhibited high sensitivity (gauge factor of −1.1), low hysteresis (maximum hysteresis error 3.2%) and ease of shaping in custom designs through a facile manufacturing process. To test the developed flexible sensor, two applicative scenarios covering the whole working range have been considered: the recording of the chest wall expansion during respiratory activity and the capture of the elbow flexion/extension movements. Full article

(This article belongs to the Special Issue Novel Sensing Technologies for Digital Health)

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24 pages, 2112 KiB

Open AccessArticle

An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques

by Krishnan Arumugasamy Muthukumar, Mondher Bouazizi and Tomoaki Ohtsuki

Sensors 2022, 22(10), 3898; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103898 - 20 May 2022

Cited by 2 | Viewed by 2514

Abstract

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply [...] Read more.

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively. Full article

(This article belongs to the Special Issue Novel Sensing Technologies for Digital Health)

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Review

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33 pages, 474 KiB

Open AccessReview

Utilising Emotion Monitoring for Developing Music Interventions for People with Dementia: A State-of-the-Art Review

by Jessica G. J. Vuijk, Jeroen Klein Brinke and Nikita Sharma

Sensors 2023, 23(13), 5834; https://0-doi-org.brum.beds.ac.uk/10.3390/s23135834 - 22 Jun 2023

Viewed by 1748

Abstract

The demand for smart solutions to support people with dementia (PwD) is increasing. These solutions are expected to assist PwD with their emotional, physical, and social well-being. At the moment, state-of-the-art works allow for the monitoring of physical well-being; however, not much attention [...] Read more.

The demand for smart solutions to support people with dementia (PwD) is increasing. These solutions are expected to assist PwD with their emotional, physical, and social well-being. At the moment, state-of-the-art works allow for the monitoring of physical well-being; however, not much attention is delineated for monitoring the emotional and social well-being of PwD. Research on emotion monitoring can be combined with research on the effects of music on PwD given its promising effects. More specifically, knowledge of the emotional state allows for music intervention to alleviate negative emotions by eliciting positive emotions in PwD. In this direction, the paper conducts a state-of-the-art review on two aspects: (i) the effect of music on PwD and (ii) both wearable and non-wearable sensing systems for emotional state monitoring. After outlining the application of musical interventions for PwD, including emotion monitoring sensors and algorithms, multiple challenges are identified. The main findings include a need for rigorous research approaches for the development of adaptable solutions that can tackle dynamic changes caused by the diminishing cognitive abilities of PwD with a focus on privacy and adoption aspects. By addressing these requirements, advancements can be made in harnessing music and emotion monitoring for PwD, thereby facilitating the creation of more resilient and scalable solutions to aid caregivers and PwD. Full article

(This article belongs to the Special Issue Novel Sensing Technologies for Digital Health)

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