This paper proposed an innovative mechanical design using the Rocker-bogie mechanism for resilient Trash-Collecting Robots. Mask-RCNN, YOLOV4, and YOLOv4-tiny were experimented on and analyzed for trash detection. The Trash-Collecting Robot was developed to be completely autonomous as it was able to detect trash, move towards it, and pick it up while avoiding any obstacles along the way. Sensors including a camera, ultrasonic sensor, and GPS module played an imperative role in automation. The brain of the Robot, namely, Raspberry Pi and Arduino, processed the data from the sensors and performed path-planning and consequent motion of the robot through actuation of motors. Three models for object detection were tested for potential use in the robot: Mask-RCNN, YOLOv4, and YOLOv4-tiny. Mask-RCNN achieved an average precision (mAP) of over 83% and detection time (DT) of 3973.29 ms, YOLOv4 achieved 97.1% (mAP) and 32.76 DT, and YOLOv4-tiny achieved 95.2% and 5.21 ms DT. The YOLOv4-tiny was selected as it offered a very similar mAP to YOLOv4, but with a much lower DT. The design was simulated on different terrains and behaved as expected.

Due to the increasing number of COVID-19 cases, there is a remarkable demand for robots, especially in the clinical sector. SARS-CoV-2 mainly propagates due to close human interactions and contaminated surfaces, and hence, maintaining social distancing has become a mandatory preventive measure. This generates the need to treat patients with minimal doctor-patient interaction. Introducing robots in the healthcare sector protects the frontline healthcare workers from getting exposed to the coronavirus as well as decreases the need for medical personnel as robots can partially take over some medical roles. The aim of this paper is to highlight the emerging role of robotic applications in the healthcare sector and allied areas. To this end, a systematic review was conducted regarding the various robots that have been implemented worldwide during the COVID-19 pandemic to attenuate and contain the virus. The results obtained from this study reveal that the implementation of robotics into the healthcare field has a substantial effect in controlling the spread of SARS-CoV-2, as it blocks coronavirus propagation between patients and healthcare workers, along with other advantages such as disinfection or cleaning.

Background: The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. Methods: A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. Results: A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [⁶⁸Ga]Ga-PSMA-11, [¹⁸F]DCFPyl, and [¹⁸F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. Conclusion: DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.

Wearable technology has added a whole new dimension to already rich field of personal gadgets. It was the mobile phone which truly made gadgets personal. With so many services built around mobile phones, the market has opened up for a whole new personalization experience involving wearable technology. The fabric sensors that sense stretch, pressure, bend and even the direction of bracing on the body are now combined with wearable microcontrollers such as flora and lily pad. The connections between them are being made from conductive threads which curves along with the fabric. In this paper we will take a peek as to how different teams have applied the rich knowledge of wearable technologies to achieve their goals.

According to CNN-Health and as of June 2021, authorities have claimed over 174.4 million coronavirus cases and 3.8 million deaths since China reported the first case in December 2019. The objective of the project “MedBuddy” is to reduce the number of coronavirus cases among the medical staff and doctors when they must tend to the patients and administering the medications who are isolated in the general ward. This objective is achieved by designing and developing a Bluetooth controlled robot car built using the Arduino Uno microcontroller and equipped with a spare smartphone whose camera will be used to provide live feed to the application developed using MIT App Inventor. The robot is equipped with tray that allows the medicines to be delivered with an ease to the patients from a safe distance. This will result in making the timely delivery of medicines for the patients and will also reduce the unnecessary contact with the patients, minimizing the risk for medical personnel.

Complex human activity is hard to classify with observational logic. The advent of fabric sensors with discernible and steady response have opened a new avenue for classifying physical activity of humans. Our goal is to construct a smart jacket for human activity/posture classification and to apply machine learning models on the fabric sensor reading to predict physical events. The core concept is in placing stretch sensors, pressure sensors and accelerometer at strategic location to collect the responses. The sensor's response is studied toidentify linearity and repeatability, using which, reliability of data is determined. Further, appropriate Machine learning algorithms can be employed to classify different set of activities. It also important to follow a proper procedure to record data from fabric sensors which create a voltage fluctuation when stretched. We propose a systematic way of design, development, testing and integration of fabric sensors for reliable data collection in this paper.