The need for faster and more accessible alternatives to laboratory microscopy is driving many innovations throughout the image and data acquisition chain in the biomedical field. Benchtop microscopes are bulky, lack communications capabilities, and require trained personnel for analysis. New technologies, such as compact 3D-printed devices integrated with the Internet of Things (IoT) for data sharing and cloud computing, as well as automated image processing using deep learning algorithms, can address these limitations and enhance the conventional imaging workflow. This review reports on recent advancements in microscope miniaturization, with a focus on emerging technologies such as photoacoustic microscopy and more established approaches like smartphone-based microscopy. The potential applications of IoT in microscopy are examined in detail. Furthermore, this review discusses the evolution of image processing in microscopy, transitioning from traditional to deep learning methods that facilitate image enhancement and data interpretation. Despite numerous advancements in the field, there is a noticeable lack of studies that holistically address the entire microscopy acquisition chain. This review aims to highlight the potential of IoT and artificial intelligence (AI) in combination with portable microscopy, emphasizing the importance of a comprehensive approach to the microscopy acquisition chain, from portability to image analysis.

Treatment planning and monitoring for motor neurorehabilitation still relies mostly on coarse clinical scales and clinician observation. Distribution analysis of action patterns recorded by sensors can provide a quantitative and easily visualizable representation of complex individualized dynamics. Here, we review the use of distributions to analyze human action in health and following neurologic injury. The aim is to demonstrate the potential of distribution analysis as a simple yet effective framework for scanning a patient prior to therapy, assisting clinicians in quantitative evaluations, and thereby uncovering underlying pathophysiological mechanisms to optimize therapy. In this review paper, we first describe how idealized distributions can model movement and capture spatiotemporal patterns. We then review metrics that can holistically quantify changes between distributions. Next, we highlight applications in different data domains that have used these approaches and precise comparison techniques to identify motor deficits and personalize therapy. Together, these methods have uncovered upper extremity flexion synergy patterns in half of stroke survivors, improved classification accuracy in data-driven models, and reduced movement errors threefold. Finally, we discuss the challenges and opportunities facing the application of distribution analysis for neurorehabilitation and precision medicine.

The tongue is a uniquely agile muscular structure essential for vital tasks of speech, breathing, chewing, and swallowing, functions commonly disrupted following neurological injury. Yet, current rehabilitation approaches lack objective measures and techniques to characterize impairment and restore the tongue’s ability. Here, we introduce a clinic-friendly method that isolates and quantifies tongue agility, defined as the ability to execute rapid and precise movements, using a wireless intraoral sensing device that provides real-time visual feedback of movement. Six participants diagnosed with dysarthria completed seven one-hour intervention sessions. Tongue movement probability distributions were generated to identify individualized deviations from neurotypical patterns. An individualized visual feedback intervention was designed to redirect movement away from over-expressed regions toward under-expressed deficient areas. Across the intervention, sensing area coverage increased significantly by 10.29 %, while over-expressed areas decreased significantly by 3.99 %, and movement velocity improved significantly by 3.85 %. This pilot study provides promising preliminary evidence that precision visual feedback rehabilitation can reshape tongue movement patterns and enhance tongue agility in individuals with oral motor disorders.