Recent technological advances enable gripper-equipped robots to perform many tasks traditionally associated with the human hand, allowing the use of grippers in a wide range of applications. Depending on the application, an ideal gripper design should be affordable, energy-efficient, and adaptable to many situations. However, regardless of the number of grippers available on the market, there are still many tasks that are difficult for grippers to perform, which indicates the demand and room for new designs to compete with the human hand. Thus, this paper provides a comprehensive review of robotic arm grippers to identify the benefits and drawbacks of various gripper designs. The research compares gripper designs by considering the actuation mechanism, degrees of freedom, grasping capabilities with multiple objects, and applications, concluding which should be the gripper design with the broader set of capabilities.

Being an emerging technology, robotic manipulation has encountered tremendous advancements due to technological developments starting from using sensors to artificial intelligence. Over the decades, robotic manipulation has advanced in terms of the versatility and flexibility of mobile robot platforms. Thus, robots are now capable of interacting with the world around them. To interact with the real world, robots require various sensory inputs from their surroundings, and the use of vision is rapidly increasing nowadays, as vision is unquestionably a rich source of information for a robotic system. In recent years, robotic manipulators have made significant progress towards achieving human-like abilities. There is still a large gap between human and robot dexterity, especially when it comes to executing complex and long-lasting manipulations. This paper comprehensively investigates the state-of-the-art development of vision-based robotic application, which includes the current state, components, and approaches used along with the algorithms with respect to the control and application of robots. Furthermore, a comprehensive analysis of those vision-based applied algorithms, their effectiveness, and their complexity has been enlightened here. To conclude, there is a discussion over the constraints while performing the research and potential solutions to develop a robust and accurate vision-based robot manipulation.

Upper limb dysfunction (ULD) is common following a stroke, spinal cord injury, trauma, and occupational accidents. Post-stroke patients with ULD need long-term assistance from therapists for their rehabilitation, which generally occurs at the hospital or outpatient clinic. Physical therapists are unavailable because of geographical, financial, and scheduling concerns, and continuity of care needs to be improved due to the need to travel to multiple locations for therapy. As a result, providing specific, tailored therapy programs is challenging due to the absence of feedback and real-time monitoring. An effective telerehabilitation system can address this issue and is more cost-effective for healthcare providers and patients than traditional inpatient or person-to-person rehabilitation. Remotely operating robotic devices and using advanced technology improves patient and healthcare provider safety and reduces injuries. In this study, we developed a novel telerehabilitation framework for rehabilitation robots utilizing PTC’s Industrial Internet of Things (IIoT) platform to remotely provide robot-aided therapies for individuals with ULD. With the developed telerehabilitation framework, an operator can teleoperate the rehab robots to deliver Upper-limb (UL) exercises via an Augmented Reality (AR) based graphical user interface (GUI). This AR platform communicates bidirectionally using ThingWorx IIOT. It leverages the digital twin (DT) structure facilitated by Vuforia studio to visualize the physical robot motions happening in remote places. The telerehabilitation framework was validated through a commercially available robot (xArm 5), an exoskeleton (SREx), and an end-effector type rehabilitation robot (DMRbot) developed at Biorobotics Lab, UWM. The experiment results show that the telerehabilitation system can successfully provide UL rehab exercises in 2D and 3D planes via AR. The proposed framework is developed to facilitate robust and more promising robot-aided rehabilitation sessions remotely, and it can also be applied in other medical applications.

Background: There is worldwide demand for an affordable hemoglobin measurement solution, which is a particularly urgent need in developing countries. The smartphone, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Consideration of a smartphone-based hemoglobin measurement tool is compelling because of the possibilities for an affordable, portable, and reliable point-of-care tool by leveraging the camera capacity, computing power, and lighting sources of the smartphone. However, several smartphone-based hemoglobin measurement techniques have encountered significant challenges with respect to data collection methods, sensor selection, signal analysis processes, and machine-learning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device. Objective: In this study, we analyzed existing invasive, minimally invasive, and noninvasive approaches for blood hemoglobin level measurement with the goal of recommending data collection techniques, signal extraction processes, feature calculation strategies, theoretical foundation, and machine-learning algorithms for developing a noninvasive hemoglobin level estimation point-of-care tool using a smartphone. Methods: We explored research papers related to invasive, minimally invasive, and noninvasive hemoglobin level measurement processes. We investigated the challenges and opportunities of each technique. We compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, machine-learning algorithms, and prediction models to calculate hemoglobin levels. This analysis was then used to recommend realistic approaches to build a smartphone-based point-of-care tool for hemoglobin measurement in a noninvasive manner. Results: The fingertip area is one of the best data collection sites from the body, followed by the lower eye conjunctival area. Near-infrared (NIR) light-emitting diode (LED) light with wavelengths of 850 nm, 940 nm, and 1070 nm were identified as potential light sources to receive a hemoglobin response from living tissue. PPG signals from fingertip videos, captured under various light sources, can provide critical physiological clues. The features of PPG signals captured under 1070 nm and 850 nm NIR LED are considered to be the best signal combinations following a dual-wavelength theoretical foundation. For error metrics presentation, we recommend the mean absolute percentage error, mean squared error, correlation coefficient, and Bland-Altman plot. Conclusions: We addressed the challenges of developing an affordable, portable, and reliable point-of-care tool for hemoglobin measurement using a smartphone. Leveraging the smartphone’s camera capacity, computing power, and lighting sources, we define specific recommendations for practical point-of-care solution development. We further provide recommendations to resolve several long-standing research questions, including how to capture a signal using a smartphone camera, select the best body site for signal collection, and overcome noise issues in the smartphone-captured signal. We also describe the process of extracting a signal’s features after capturing the signal based on fundamental theory. The list of machine-learning algorithms provided will be useful for processing PPG features. These recommendations should be valuable for future investigators seeking to build a reliable and affordable hemoglobin prediction model using a smartphone.

Background: Using remote monitoring technology in the context of Alzheimer disease (AD) care presents exciting new opportunities to lessen caregiver stress and improve patient care quality. The application of wearables, environmental sensors, and smart home systems designed specifically for patients with AD represents a promising interdisciplinary approach that integrates advanced technology with health care to enhance patient safety, monitor health parameters in real time, and provide comprehensive support to caregivers. Objective: The objectives of this study included evaluating the effectiveness of various remote sensing technologies in enhancing patient outcomes and identifying strategies to alleviate the burden on health care professionals and caregivers. Critical elements such as regulatory compliance, user-centered design, privacy and security considerations, and the overall efficacy of relevant technologies were comprehensively examined. Ultimately, this study aimed to propose a comprehensive remote monitoring framework tailored to the needs of patients with AD and related dementias. Methods: Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, we conducted a systematic review on remote monitoring for patients with AD and related dementias. Our search spanned 4 major electronic databases—Google Scholar, PubMed, IEEE Xplore, and DBLP on February 20, 2024, with an updated search on May 18, 2024. Results: A total of 31 publications met the inclusion criteria, highlighting 4 key research areas: existing remote monitoring technologies, balancing practicality and empathy, security and privacy in monitoring, and technology design for AD care. The studies revealed a strong focus on various remote monitoring methods for capturing behavioral, physiological, and environmental data yet showed a gap in evaluating these methods for patient and caregiver needs, privacy, and usability. The findings also indicated that many studies lacked robust reference standards and did not consistently apply critical appraisal criteria, underlining the need for comprehensive frameworks that better integrate these essential considerations. Conclusions: This comprehensive literature review of remote monitoring technologies for patients with AD provides an understanding of remote monitoring technologies, trends, and gaps in the current research and the significance of novel strategies for remote monitoring to enhance patient outcomes and reduce the burden among health professionals and caregivers. The proposed remote monitoring framework aims to inspire the development of new interdisciplinary research models that advance care for patients with AD.