The objective of this study was to evaluate differences in driving performance, visual detection performance, and eye-scanning behavior between glaucoma patients and control participants without glaucoma. Glaucoma patients (n = 23) and control participants (n = 12) completed four 5-min driving sessions in a simulator. The participants were instructed to maintain the car in the right lane of a two-lane highway while their speed was automatically maintained at 100 km/h. Additional tasks per session were: Session 1: none, Session 2: verbalization of projected letters, Session 3: avoidance of static obstacles, and Session 4: combined letter verbalization and avoidance of static obstacles. Eye-scanning behavior was recorded with an eye-tracker. Results showed no statistically significant differences between patients and control participants for lane keeping, obstacle avoidance, and eye-scanning behavior. Steering activity, number of missed letters, and letter reaction time were significantly higher for glaucoma patients than for control participants. In conclusion, glaucoma patients were able to avoid objects and maintain a nominal lane keeping performance, but applied more steering input than control participants, and were more likely than control participants to miss peripherally projected stimuli. The eye-tracking results suggest that glaucoma patients did not use extra visual search to compensate for their visual field loss. Limitations of the study, such as small sample size, are discussed.

Proper waste separation is essential for recycling. However, it can be challenging to identify waste materials accurately, especially in real-world settings. In this study, a systematic literature review (SLR) was carried out to identify the physical enablers (sensors and computing devices), datasets, and machine learning (ML) algorithms used for waste identification in indirect separation systems. This review analyzed 55 studies, following the Kitchenham guidelines. The SLR identified three levels of autonomy in waste segregation systems: full, moderate, and low. Edge computing devices are the most widely used for data processing (9 of 17 studies). Five types of sensors are used for waste identification: inductive, capacitive, image-based, sound-based, and weight-based sensors. Visible-image-based sensors are the most common in the literature. Single classification is the most popular dataset type (65%), followed by bounding box detection (22.5%). Convolutional neural networks (CNNs) are the most commonly used ML technique for waste identification (24 out of 26 articles). One of the main conclusions is that waste identification faces challenges with real-world complexity, limited data in datasets, and a lack of detailed waste categorization. Future work in waste identification should focus on deployment and testing in non-controlled environments, expanding system functionalities, and exploring sensor fusion.

This article aims to clarify the current state-of-the-art of robotic/mechanical devices for post-stroke thumb rehabilitation as well as the anatomical characteristics and motions of the thumb that are crucial for the development of any device that aims to support its motion. A systematic literature search was conducted to identify robotic/mechanical devices for post-stroke thumb rehabilitation. Specific electronic databases and well-defined search terms and inclusion/exclusion criteria were used for such purpose. A reasoning model was devised to support the structured abstraction of relevant data from the literature of interest. Following the main search and after removing duplicated and other non-relevant studies, 68 articles (corresponding to 32 devices) were left for further examination. These articles were analyzed to extract data relative to (i) the motions assisted/permitted - either actively or passively - by the device per anatomical joint of the thumb and (ii) mechanical-related aspects (i.e., architecture, connections to thumb, other fingers supported, adjustability to different hand sizes, actuators - type, quantity, location, power transmission and motion trajectory). Most articles describe preliminary design and testing of prototypes, rather than the thorough evaluation of commercially ready devices. Defining appropriate kinematic models of the thumb upon which to design such devices still remains a challenging and unresolved task. Further research is needed before these devices can actually be implemented in clinical environments to serve their intended purpose of complementing the labour of therapists by facilitating intensive treatment with precise and repeatable exercises. Implications for Rehabilitation Post-stroke functional disability of the hand, and particularly of the thumb, significantly affects the capability to perform activities of daily living, threatening the independence and quality of life of the stroke survivors. The latest studies show that a high-dose intensive therapy (in terms of frequency, duration and intensity/effort) is the key to effectively modify neural organization and recover the motor skills that were lost after a stroke. Conventional therapy based on manual interaction with physical therapists makes the procedure labour intensive and increases the costs. Robotic/mechanical devices hold promise for complementing conventional post-stroke therapy. Specifically, these devices can provide reliable and accurate therapy for long periods of time without the associated fatigue. Also, they can be used as a means to assess patients? performance and progress in an objective and consistent manner. The full potential of robot-assisted therapy is still to be unveiled. Further exploration will surely lead to devices that can be well accepted equally by therapists and patients and that can be useful both in clinical and home-based rehabilitation practice such that motor recovery of the hand becomes a common outcome in stroke survivors. This overview provides the reader, possibly a designer of such a device, with a complete overview of the state-of-the-art of robotic/mechanical devices consisting of or including features for the rehabilitation of the thumb. Also, we clarify the anatomical characteristics and motions of the thumb that are crucial for the development of any device that aims to support its motion. Hopefully, this?combined with the outlined opportunities for further research?leads to the improvement of current devices and the development of new technology and knowledge in the field.