A framework for design and usability testing of telerehabilitation system for adults with chronic diseases: A panoramic scoping review.

Objective: This scoping review aimed to identify the design and usability testing of a telerehabilitation (TR) system, and its characteristics and functionalities that are best-suited for rehabilitating adults with chronic diseases. Methods: Searches were conducted in PubMed, EBSCO, Web of Science, and Cochrane library for studies published between January 2017 and December 2022. We followed the Joanna Briggs Institute guidelines and the framework by Arksey and O'Malley. Screening was undertaken by two reviewers, and data extraction was undertaken by the first author. Then, the data were further reviewed and discussed thoroughly with the team members. Results: A total of 31 results were identified, with the core criteria of developing and testing a telerehabilitation system, including a mobile app for cardiovascular diseases, cancer, diabetes, and chronic respiratory disorders. All developed systems resulted from multidisciplinary teams and employed mixed-methods research. We proposed the "input-process-output" framework that identified phases of both system design and usability testing. Through system design, we reported the use of user-centered design, iterative design, users' needs and characteristics, theory underpinning development, and the expert panel in 64%, 75%, 86%, 82%, and 71% of the studies, respectively. We recorded the application of moderated usability testing, unmoderated testing (1), and unmoderated testing (2) in 74%, 63%, and 15% of the studies, respectively. The identified design and testing activities produced a matured system, a high-fidelity prototype, and a released system in 81.5%, 15%, and 3.5%, respectively. Conclusion: This review provides a framework for TR system design and testing for a wide range of chronic diseases that require prolonged management through remote monitoring using a mobile app. The identified "input-process-output" framework highlights the inputs, design, development, and improvement as components of the system design. It also identifies the "moderated-unmoderated" model for conducting usability testing. This review illustrates characteristics and functionalities of the TR systems and healthcare professional roles.

Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models.

Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation of tumors are extremely time-consuming but crucial tasks due to the tumor's pattern. In this paper, we propose a new lightweight segmentation model called MicrowaveSegNet (MSegNet), which segments the brain tumor, and a new classifier called the BrainImageNet (BINet) model to classify the RMW images. Initially, three hundred (300) RMW brain image samples were obtained from our sensors-based microwave brain imaging (SMBI) system to create an original dataset. Then, image preprocessing and augmentation techniques were applied to make 6000 training images per fold for a 5-fold cross-validation. Later, the MSegNet and BINet were compared to state-of-the-art segmentation and classification models to verify their performance. The MSegNet has achieved an Intersection-over-Union (IoU) and Dice score of 86.92% and 93.10%, respectively, for tumor segmentation. The BINet has achieved an accuracy, precision, recall, F1-score, and specificity of 89.33%, 88.74%, 88.67%, 88.61%, and 94.33%, respectively, for three-class classification using raw RMW images, whereas it achieved 98.33%, 98.35%, 98.33%, 98.33%, and 99.17%, respectively, for segmented RMW images. Therefore, the proposed cascaded model can be used in the SMBI system.

Microwave brain imaging system to detect brain tumor using metamaterial loaded stacked antenna array.

In this paper, proposes a microwave brain imaging system to detect brain tumors using a metamaterial (MTM) loaded three-dimensional (3D) stacked wideband antenna array. The antenna is comprised of metamaterial-loaded with three substrate layers, including two air gaps. One 1 × 4 MTM array element is used in the top layer and middle layer, and one 3 × 2 MTM array element is used in the bottom layer. The MTM array elements in layers are utilized to enhance the performance concerning antenna's efficiency, bandwidth, realized gain, radiation directionality in free space and near the head model. The antenna is fabricated on cost-effective Rogers RT5880 and RO4350B substrate, and the optimized dimension of the antenna is 50 × 40 × 8.66 mm3. The measured results show that the antenna has a fractional bandwidth of 79.20% (1.37-3.16 GHz), 93% radiation efficiency, 98% high fidelity factor, 6.67 dBi gain, and adequate field penetration in the head tissue with a maximum of 0.0018 W/kg specific absorption rate. In addition, a 3D realistic tissue-mimicking head phantom is fabricated and measured to verify the performance of the antenna. Later, a nine-antenna array-based microwave brain imaging (MBI) system is implemented and investigated by using phantom model. After that, the scattering parameters are collected, analyzed, and then processed by the Iteratively Corrected delay-multiply-and-sum algorithm to detect and reconstruct the brain tumor images. The imaging results demonstrated that the implemented MBI system can successfully detect the target benign and malignant tumors with their locations inside the brain.

Influence of drone carriage material on maintenance of storage temperature and quality of blood samples during transportation in an equatorial climate.

The disruptive potentials of drones are rapidly growing including for the delivery of blood samples in healthcare. Maintenance of the quality of blood samples is important to ascertain that the drone is a safe mode of transportation, particularly during emergencies and in critical cases. The influence of the drone carriage material on blood samples transportation was investigated in this study. Two phases of drone simulation flights were conducted in Cyberjaya, Malaysia. In Phase 1, the effect of drone carriage material on the internal storage temperature during blood samples transportation was determined. Three types of carriage materials were compared: aluminium, expanded polystyrene (EPS) foam, and polypropylene (PP) plastic. In Phase 2, the quality of drone-transported blood samples was assessed, using the best material from Phase 1 as the drone carriage material. Biochemical and hematological analyses of 60 blood samples were conducted using five parameters. In Phase 1, EPS foam was found to be the best material to maintain a stable and favorable internal storage temperature at mean kinetic temperature ±SD of 4.70 ±1.14°C. Much higher and unfavorable mean kinetic temperatures were recorded for aluminium (11.46 ±0.35°C) and plastic (14.17 ±0.05°C). In Phase 2, laboratory tests show that the quality of blood samples was well maintained, and the mean biochemical and hematological parameters of drone-transported blood samples showed no significant alteration compared to ground controls. Drone carriage material is an important determinant of the quality of blood samples transported by drone, particularly in hot equatorial climates as in Malaysia. The blood storage temperature was best maintained using EPS foam, as evidenced by the favorable average temperature and preservation of hematological and biochemical parameters of the blood samples.

Finite element analysis of the wrist in stroke patients: the effects of hand grip.

The provision of the most suitable rehabilitation treatment for stroke patient remains an ongoing challenge for clinicians. Fully understanding the pathomechanics of the upper limb will allow doctors to assist patients with physiotherapy treatment that will aid in full arm recovery. A biomechanical study was therefore conducted using the finite element (FE) method. A three-dimensional (3D) model of the human wrist was reconstructed using computed tomography (CT)-scanned images. A stroke model was constructed based on pathological problems, i.e. bone density reductions, cartilage wane, and spasticity. The cartilages were reconstructed as per the articulation shapes in the joint, while the ligaments were modelled using linear links. The hand grip condition was mimicked, and the resulting biomechanical characteristics of the stroke and healthy models were compared. Due to the lower thickness of the cartilages, the stroke model reported a higher contact pressure (305 MPa), specifically at the MC1-trapezium. Contrarily, a healthy model reported a contact pressure of 228 MPa. In the context of wrist extension and displacement, the stroke model (0.68° and 5.54 mm, respectively) reported a lower magnitude than the healthy model (0.98° and 9.43 mm, respectively), which agrees with previously reported works. It was therefore concluded that clinicians should take extra care in rehabilitation treatment of wrist movement in order to prevent the occurrence of other complications. Graphical abstract ᅟ.

Experimental feasibility study of estimation of the normalized central blood pressure waveform from radial photoplethysmogram.

The feasibility of a novel system to reliably estimate the normalized central blood pressure (CBPN) from the radial photoplethysmogram (PPG) is investigated. Right-wrist radial blood pressure and left-wrist PPG were simultaneously recorded in five different days. An industry-standard applanation tonometer was employed for recording radial blood pressure. The CBP waveform was amplitude-normalized to determine CBPN. A total of fifteen second-order autoregressive models with exogenous input were investigated using system identification techniques. Among these 15 models, the model producing the lowest coefficient of variation (CV) of the fitness during the five days was selected as the reference model. Results show that the proposed model is able to faithfully reproduce CBPN (mean fitness = 85.2% ± 2.5%) from the radial PPG for all 15 segments during the five recording days. The low CV value of 3.35% suggests a stable model valid for different recording days.

Applicability of adaptive noise cancellation to fetal heart rate detection using photoplethysmography.

In this paper, an approach based on adaptive noise cancellation (ANC) is evaluated for extraction of the fetal heart rate using photoplethysmographic signals from the maternal abdomen. A simple optical model is proposed in which the maternal and fetal blood pulsations result in emulated signals where the lower SNR limit (fetal to maternal) is -25dB. It is shown that a recursive least-squares algorithm is capable of extracting the peaks of the fetal PPG from these signals, for typical values of maternal and fetal tissues.