A Comprehensive Survey of Depth Completion Approaches.

Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the earlier approaches focused on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have divided the literature into two major categories; unguided methods and image-guided methods. The latter is further subdivided into multi-branch and spatial propagation networks. The multi-branch networks further have a sub-category named image-guided filtering. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review in detail different state-of-the-art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.

DBGC: Dimension-Based Generic Convolution Block for Object Recognition.

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs.

Allgrove syndrome: case report of 7 years old boy from Bahawalpur.

Allgrove syndrome is a rare autosomal recessive syndrome of unknown prevalence. The first case of Allgrove syndrome was reported in 1978 by Allgrove. It is characterized by triad of achalasia, alacrima and adrenal hypoplasia. There are also associated autonomic and neurological manifestations. We report the case of a 7 years old boy being treated for achalasia cardia, presented with fits and altered sensorium which on further investigations was found to be due to adrenal insensitivity (Raised ACTH level, low Cortisol level, and normal Aldosterone and Renin ratio). He also had undiagnosed alacrima since birth, mild degree of hearing loss and autonomic instability in the form of episodic hypertension.

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson.

BACKGROUND AND OBJECTIVES: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention. METHOD: The AiCarePWP is a wearable electronics device designed to identify instances when a patient is on the brink of experiencing a freezing episode and subsequently deliver a brief electrical impulse to the patient's shank muscles to stimulate movement. The AiCarePWP wearable device aims to identify impending freezing episodes in PD patients and deliver brief electrical impulses to stimulate movement. The study validates this innovative approach using plantar insoles with a 3D accelerometer and electrical stimulator, analysing data from the inertial measuring unit and plantar-pressure foot data to detect and predict FoG. RESULTS: Using a Convolutional Neural Network-based model, the study evaluated 47 gait features for their ability to differentiate resting, standing, and walking conditions. Variable selection was based on sensitivity, specificity, and overall accuracy, followed by Principal Component Analysis and Varimax rotation to extract and interpret factors. Factors with eigenvalues exceeding 1.0 were retained, and 37 features were retained. CONCLUSION: This study validates CNN's effectiveness in detecting FoG during various activities. It introduces a novel cueing method using electrical stimulation, which improves gait function and reduces FoG incidence in PD patients. Trustworthy wearable devices, based on Artificial Intelligence of Things (AIoT) and Artificial Intelligence of Medical Things (AIoMT), have been developed to support such interventions.

Elucidating an efficient super-capacitive response of a Sr2Ni2O5/rGO composite as an electrode material in supercapacitors.

Mixed transition metal oxides have emerged as efficient electrode materials because of their significant cycling stability, and superior capacitance values, resulting in remarkable electrochemical outputs. In this regard, Sr2Ni2O5/rGO composites were synthesized using a facile solvothermal method to achieve efficient electrochemical pursuits. X-ray diffraction confirmed the formation of finely crystallized samples with the phase evolution from orthorhombic to hexagonal. Morphological studies using field emission scanning electron microscopy depicted the desired porosity in samples with well-defined shapes and sizes of homogeneously distributed grains. Elemental analysis verified the pictorial depiction of sample compositions in terms of their stoichiometric ratios. The composite sample with composition Sr2Ni2O5@15%rGO exhibited superior electrochemical performance compared to other samples, depicting the highest specific capacitance of 148.09 F g-1 at a lower scan rate of 0.005 V s-1 observed via cyclic voltammetry. In addition, the cyclability performance of Sr2Ni2O5@15%rGO exhibits 68.5% capacitive retention after 10 000 cycles. The energy density as determined using a two-electrode system remained 4.375 W h kg-1 for the first cycle which reduced to 1.875 W h kg-1 for the 10 000th cycle, with a maximum power density of 1.25 W kg-1. The Nyquist plot represented less barrier to charge transfer. The electrode with particular composition Sr2Ni2O5@15%rGO emerged as significant, exhibiting a superior surface capacitive charge storage, that makes it a potential candidate as an electrode material.

A systematic review on functional electrical stimulation based rehabilitation systems for upper limb post-stroke recovery.

Background: Stroke is one of the most common neurological conditions that often leads to upper limb motor impairments, significantly affecting individuals' quality of life. Rehabilitation strategies are crucial in facilitating post-stroke recovery and improving functional independence. Functional Electrical Stimulation (FES) systems have emerged as promising upper limb rehabilitation tools, offering innovative neuromuscular reeducation approaches. Objective: The main objective of this paper is to provide a comprehensive systematic review of the start-of-the-art functional electrical stimulation (FES) systems for upper limb neurorehabilitation in post-stroke therapy. More specifically, this paper aims to review different types of FES systems, their feasibility testing, or randomized control trials (RCT) studies. Methods: The FES systems classification is based on the involvement of patient feedback within the FES control, which mainly includes "Open-Loop FES Systems" (manually controlled) and "Closed-Loop FES Systems" (brain-computer interface-BCI and electromyography-EMG controlled). Thus, valuable insights are presented into the technological advantages and effectiveness of Manual FES, EEG-FES, and EMG-FES systems. Results and discussion: The review analyzed 25 studies and found that the use of FES-based rehabilitation systems resulted in favorable outcomes for the stroke recovery of upper limb functional movements, as measured by the FMA (Fugl-Meyer Assessment) (Manually controlled FES: mean difference = 5.6, 95% CI (3.77, 7.5), P < 0.001; BCI-controlled FES: mean difference = 5.37, 95% CI (4.2, 6.6), P < 0.001; EMG-controlled FES: mean difference = 14.14, 95% CI (11.72, 16.6), P < 0.001) and ARAT (Action Research Arm Test) (EMG-controlled FES: mean difference = 11.9, 95% CI (8.8, 14.9), P < 0.001) scores. Furthermore, the shortcomings, clinical considerations, comparison to non-FES systems, design improvements, and possible future implications are also discussed for improving stroke rehabilitation systems and advancing post-stroke recovery. Thus, summarizing the existing literature, this review paper can help researchers identify areas for further investigation. This can lead to formulating research questions and developing new studies aimed at improving FES systems and their outcomes in upper limb rehabilitation.

Effect of Alloying Elements on the Compressive Mechanical Properties of Biomedical Titanium Alloys: A Systematic Review.

Due to problems such as the stress-shielding effect, strength-ductility trade-off dilemma, and use of rare-earth, expensive elements with high melting points in Ti alloys, the need for the design of new Ti alloys for biomedical applications has emerged. This article reports the effect of various alloying elements on the compressive mechanical performance of Ti alloys for biomedical applications for the first time as a systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines on this subject. The search strategy in this systematic review used Scopus, Web of Science, and PubMed databases and searched the articles using (Beta-type OR ß) AND Titanium AND (Mechanical property OR Microstructure) AND Alloying element keywords. Original articles from 2016 to 2022 published in English have been selected for this study as per the inclusion criteria. The results have shown that Nb can be used as the primary alloying element with Ti as it is a strong ß-stabilizer element which also reduces the elastic modulus of Ti alloys. The ß-eutectic elements (Fe, Cr, and Mn) have also emerged as cost-effective alloying elements that could improve the mechanical performance of Ti alloys. Ti-Nb-Zr-Ta alloyed with Si has shown potential to withstand the strength-ductility trade-off dilemma. The combination of a Ti-Nb binary alloy has emerged as an attractive material for designing low elastic modulus Ti alloys. The mechanical performance of the Ti-Nb alloy can be further improved using the ß-eutectic (Fe, Cr, and Mn) and neutral (Zr, Sn) elements to be alloyed with a Ti-Nb binary alloy. The strength-ductility trade-off issue can be overcome using Si as an alloying element in Ti-Nb-Zr-Ta alloys.

Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review.

Objective.Stroke is one of the most common neural disorders, which causes physical disabilities and motor impairments among its survivors. Several technologies have been developed for providing stroke rehabilitation and to assist the survivors in performing their daily life activities. Currently, the use of flexible technology (FT) for stroke rehabilitation systems is on a rise that allows the development of more compact and lightweight wearable systems, which stroke survivors can easily use for long-term activities.Approach.For stroke applications, FT mainly includes the 'flexible/stretchable electronics', 'e-textile (electronic textile)' and 'soft robotics'. Thus, a thorough literature review has been performed to report the practical implementation of FT for post-stroke application.Main results.In this review, the highlights of the advancement of FT in stroke rehabilitation systems are dealt with. Such systems mainly involve the 'biosignal acquisition unit', 'rehabilitation devices' and 'assistive systems'. In terms of biosignals acquisition, electroencephalography and electromyography are comprehensively described. For rehabilitation/assistive systems, the application of functional electrical stimulation and robotics units (exoskeleton, orthosis, etc) have been explained.Significance.This is the first review article that compiles the different studies regarding FT based post-stroke systems. Furthermore, the technological advantages, limitations, and possible future implications are also discussed to help improve and advance the flexible systems for the betterment of the stroke community.

Electromyography and Inertial Motion Sensors Based Wearable Data Acquisition System for Stroke Patients: A Pilot Study.

Development of wearable data acquisition systems with applications to human-machine interaction (HMI) is of great interest to assist stroke patients or people with motor disabilities. This paper proposes a hybrid wireless data acquisition system, which combines surface electromyography (sEMG) and inertial measurement unit (IMU) sensors. It is designed to interface wrist extension with external devices, which allows the user to operate devices with hand orientations. A pilot study of the system performed on four healthy subjects has successfully produced two different control signals corresponding to wrist extensions. Preliminary results show a high correlation (0.42-0.75) between sEMG and IMU signals, thus proving the feasibility of such a system. Results also show that the developed system is robust as well as less susceptible to external interferences. The generated control signals can be used to perform real-time control of different devices in daily-life activities, such as turning ON/OFF of lights in a smart home, controlling an electric wheelchair, and other assistive devices. Such a system will help decrease the dependency of disabled people on their caretakers and empower them to perform their daily-life activities independently.

A Case of 45-Year Man Who Succumbed to Coronavirus Disease 2019 (COVID-19).

We present a case of a 45-year male with history of fever and cough, followed by shortness of breath, lethargy and confusion at home. He was a known hypertensive, smoker and naswaar addict. He had history of contact with a friend who had recently returned from China, and used to work there as a physician. On presentation, he was hypoxic with bilateral coarse crepitations; and chest radiograph showed left middle zone consolidation. Considering the current pandemic, provisional diagnosis of meningoencephilits, with bilateral pneumonia owing to COVID-19, was made. Throat swab was sent for COVID-19 polymerase chain reaction (PCR). He was managed in intensive care unit (ICU) with broad-spectrum antibiotics, hydroxychloroquine alongwith anticoagulants; but he remained hypoxic with rapidly worsening clinical course, requiring mechanical ventilation; and ultimately succumbed to the disease. Report of COVID-19 PCR came positive after 24 hours. Key words: Coronavirus disease 2019 (COVID-19), Meningoencephalitis, Pneumonia.

Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application.

Strokes are a growing cause of mortality and many stroke survivors suffer from motor impairment as well as other types of disabilities in their daily life activities. To treat these sequelae, motor imagery (MI) based brain-computer interface (BCI) systems have shown potential to serve as an effective neurorehabilitation tool for post-stroke rehabilitation therapy. In this review, different MI-BCI based strategies, including "Functional Electric Stimulation, Robotics Assistance and Hybrid Virtual Reality based Models," have been comprehensively reported for upper-limb neurorehabilitation. Each of these approaches have been presented to illustrate the in-depth advantages and challenges of the respective BCI systems. Additionally, the current state-of-the-art and main concerns regarding BCI based post-stroke neurorehabilitation devices have also been discussed. Finally, recommendations for future developments have been proposed while discussing the BCI neurorehabilitation systems.

Epidemic of Kala Pathar(Paraphenylene Diamine) Poisoning: an Emerging Threat in Southern Punjab.

OBJECTIVE: To assess cases of the spectrum of Kala Patharpoisoning in all age groups. STUDY DESIGN: Cross-sectional study. PLACE AND DURATION OF STUDY: Combined Military Hospital (CMH) Bahawalpur and Bahawal Victoria Hospital (BVH), Bahawalpur, from January 2016 to April 2017. METHODOLOGY: All the cases of Kala Pathar(Paraphenylene diamine (PPD)) poisoning, admitted and treated at the study places during said period were included in the study. The assessed variable included gender, age, education status, socioeconomic status, reason of poisoning and mortality. Chi-square was applied for qualitative variables with p-value less than 0.05 was considered significant. RESULTS: A total of 1,258 cases of PPD poisoning were included in the study; 814 (64.7%) females and 444 (35.3%) males. Their age ranged from 5 - 63 years, with median age 21 (IQR 4). Sixty-six (5.2%) were children and the rest 1,192 (94.8%) were adults. In adults 1,125 (94.37%) cases of PPD poisoning were suicidal and 62 (5.20%) accidental cases; only 5 (0.42%)adults were intentionally poisoned. On the other hand, only one child took it with suicidal intent, 54 (81.81%) ingested it accidentally and 11 (16.66%) children were given poison deliberately with the intent to murder. The overall mortality was 24.08% - 22.81% in adults, and 46.96% in children. CONCLUSION: Kala Pathar(PPD) is a lethal substance when ingested. PPD poisoning is not limited to adults; many cases of pediatric poisoning are also being reported in Southern Punjab. Mortality due to Kala Patharis high. Tracheostomy should be done immediately in all such cases; and high intensive multidisciplinary approach is required.