Smart capsules are developing at a tremendous pace with a promise to become effective clinical tools for the diagnosis and monitoring of gut health. This field emerged in the early 2000s with a successful translation of an endoscopic capsule from laboratory prototype to a commercially viable clinical device. Recently, this field has accelerated and expanded into various domains beyond imaging, including the measurement of gut physiological parameters such as temperature, pH, pressure and gas sensing, and the development of sampling devices for better insight into gut health. In this review, the status of smart capsules for sensing gut parameters is presented to provide a broad picture of these state-of-the-art devices while focusing on the technical and clinical challenges the devices need to overcome to realise their value in clinical settings. Smart capsules are developed to perform sensing operations throughout the length of the gut to better understand the body’s response under various conditions. Furthermore, the prospects of such sensing devices are discussed that might help readers, especially health practitioners, to adapt to this inevitable transformation in healthcare. As a compliment to gut sensing smart capsules, significant amount of effort has been put into the development of robotic capsules to collect tissue biopsy and gut microbiota samples to perform in-depth analysis after capsule retrieval which will be a game changer for gut health diagnosis, and this advancement is also covered in this review. The expansion of smart capsules to robotic capsules for gut microbiota collection has opened new avenues for research with a great promise to revolutionise human health diagnosis, monitoring and intervention.

Human gut microbiota can provide comprehensive information about the health of a host but the tools to collect microbiome samples are not currently available. A standalone wireless robotic capsule that has been developed in this study, collects the microbiota both from lumen (capsule surrounding) and intestinal wall (mucosa layer) for the first time. First, a two-way shape memory alloy (SMA) spring actuation system was developed by tackling the high-drain current requirement of SMAs. The actuator can produce up to 800 mN force that was sufficient to collect samples. Second, successful encapsulation of the collected sample to avoid contamination was realised by testing 3 main sealing materials. Third, the robotic capsule was tested in a gut simulator that mimics in-vivo environment to ensure successful and safe travel of the capsule along the gastrointestinal tract. Finally an in vitro experimental setup that keeps an intestine alive for 6 hours was used to optimise the sample collection. The capsule collected 128 $\mu$ L and 107 $\mu$ L samples (which are sufficient quantities for microbiome analysis) from duodenual and ileal tissues of a sheep. The proposed robotic capsule has a potential to become a vital apparatus for clinicians to sample human and animal gut in the future.

Using robotic capsules for assessing gut health has been an emerging field since the early 2000s with researchers attempting to perform diagnosis, monitoring, and therapeutic functions inside the gut. The knowledge of peristaltic forces inside the intestine are crucial for designing the actuation mechanism of robotic capsules, however, the impact of peristalsis on a capsule has not yet been quantified. In this work, an analytical model is presented to study the peristaltic movement of the small intestine. For the first time, finite element simulations were conducted in COMSOL multiphysics to generate intestinal peristaltic forces, and analyze their impact on a robotic capsule. A capsule prototype (30 × φ12 mm) was developed to measure the peristaltic forces from living intestinal tissue, while an embedded system was used simultaneously to record the live data from the capsule-intestine interaction. In in vitro experiments, the intestine applied an average axial force of 226 mN and contraction cycles of 9 times/min, while the capsule prototype experienced maximum radial force of 180 mN. A specialized in vitro setup is developed to keep fresh ex vivo intestine samples alive for up to 6 h, while the capsule prototype measured the intestinal forces from the living tissue. This in vitro experimental setup provided an excellent model for the in vivo environment in terms of generating peristaltic movements, hence this force analysis will help in developing efficient prototypes for locomotion, anchoring, localization, biopsy, drug delivery, and sampling mechanisms for robotic capsules.

Recent studies suggest that human gut microbiota can act as a bio-marker for human health. Also, it can function as a potential tool to understand stress and anxiety. However, the conventional tools have limitations acquiring samples of gut microbiota without contamination. In this work, an untethered robotic capsule prototype is developed that can actively collect the microbiota from the mucosa layer of the small intestine for the first time with the potential to avoid the upstream and downstream contamination. An analytical model for quantifying the peristaltic forces and developing two-way shape memory alloy spring actuator is presented. For the first time, a novel two-way shape memory alloy spring actuator (5 mm x $\phi ~4$ mm) is used to perform the sampling inside the gut. The spring actuator can apply 675 mN force, which is sufficient to perform in vivo sampling. A specialised experimental setup that can keep the freshly dissected intestine alive for 6 hours is utilised to test the robotic capsule. The robotic capsule prototype has collected an average of $200~\mu L$ and $112~\mu L$ sample from living pig duodenal and ileal tissues respectively i.e. in the presence of peristaltic forces. The robotic capsule was also tested on intestine of other species including cow and sheep and collected an average of $160~\mu L$ and $185~\mu L$ of content respectively from the living post-mortem tissues. The collected sample size for all the species is feasible to analyse the microbiota through next generation sequencing techniques. The experimental setup is a reliable proxy to in-vivo behaviour and the robotic capsule experimental result is promising in terms of in situ collection of microbiota.

Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing factors. This study presents a Clinical Decision Support System (CDSS) for the early detection of respiratory disorders using a one-dimensional convolutional neural network (1D-CNN) model. The ICBHI 2017 Breathing Sound Database, which contains samples of different breathing sounds, was used in this research. During pre-processing, audio clips were resampled to a uniform rate, and breathing cycles were segmented into individual instances of the lung sound. A One-Dimensional Convolutional Neural Network (1D-CNN) consisting of convolutional layers, max pooling layers, dropout layers, and fully connected layers, was designed to classify the processed clips into four categories: normal, crackles, wheezes, and combined crackles and wheezes. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. Hyperparameters were optimized using grid search with k−fold cross-validation. The model achieved an overall accuracy of 0.95, outperforming state-of-the-art methods. Particularly, the normal and crackles categories attained the highest F1-scores of 0.97 and 0.95, respectively. The model’s robustness was further validated through 5−fold and 10−fold cross-validation experiments. This research highlighted an essential aspect of diagnosing lung sounds through artificial intelligence and utilized the 1D-CNN to classify lung sounds accurately. The proposed advancement of technology shall enable medical care practitioners to diagnose lung disorders in an improved manner, leading to better patient care.