There is a significant increase in the geriatric population across the globe. With the increase in the number of geriatric people and their associated health issues, the need for larger healthcare resources is inevitable. Because of this, healthcare service-providing industries are facing a severe challenge. However, technological advancement in recent years has enabled researchers to develop intelligent devices to deal with the scarcity of healthcare resources. In this regard, the Internet of things (IoT) technology has been a boon for healthcare services industries. It not only allows the monitoring of the health parameters of geriatric patients from a remote location but also lets them live an independent life in a cost-efficient way. The current paper provides up-to-date comprehensive knowledge of IoT-based technologies for geriatric healthcare applications. The study also discusses the current trends, issues, challenges, and future scope of research in the area of geriatric healthcare using IoT technology. Information provided in this paper will be helpful to develop futuristic solutions and provide efficient cost-effective healthcare services to the needy.

In visual-motor coordination, the human brain processes visual stimuli representative of complex motion-related tasks at the occipital lobe to generate the necessary neuronal signals for the parietal and pre-frontal lobes, which in turn generates movement related plans to excite the motor cortex to execute the actual tasks. The paper introduces a novel approach to provide rehabilitative support to patients suffering from neurological damage in their pre-frontal, parietal and/or motor cortex regions. An attempt to bypass the natural visual-motor pathway is undertaken using interval type-2 fuzzy sets to generate the approximate EEG response of the damaged pre-frontal/parietal/motor cortex from the occipital EEG signals. The approximate EEG response is used to trigger a pre-trained joint coordinate generator to obtain the desired joint coordinates of the link end-points of a robot imitating the human subject. The robot arm is here employed as a rehabilitative aid in order to move each link end-points to the desired locations in the reference coordinate system by appropriately activating its links using the well-known inverse kinematics approach. The mean-square positional errors obtained for each link end-points is found within acceptable limits for all experimental subjects including subjects with partial parietal damage, indicating a possible impact of the proposed approach in rehabilitative robotics. Subjective variation in EEG features over different sessions of experimental trials is modeled here using interval type-2 fuzzy sets for its inherent power to handle uncertainty. Experiments undertaken confirm that interval type-2 fuzzy realization outperforms its classical type-1 counterpart and back-propagation neural approaches in all experimental cases, considering link positional error as a metric. The proposed research offers a new opening for the development of possible rehabilitative aids for people with partial impairment in visual-motor coordination.

Pulmonary ailments are conventionally diagnosed by spirometry. The complex forceful breathing maneuver as well as the extreme cost of spirometry renders it unsuitable in many situations. This work is aimed to facilitate an emerging direction of tidal breathing-based pulmonary evaluation by designing a novel, equitable, precise and portable device for acquisition and analysis of directional tidal breathing patterns, in real time. The proposed system primarily uses an in-house designed blow pipe, 40-kHz air-coupled ultrasound transreceivers, and a radio frequency (RF) phase-gain integrated circuit (IC). Moreover, in order to achieve high sensitivity in a cost-effective design philosophy, we have exploited the phase measurement technique, instead of selecting the contemporary time-of-flight (TOF) measurement; since application of the TOF principle in tidal breathing assessments requires sub-micro to nanosecond time resolution. This approach, which depends on accurate phase measurement, contributed to enhanced sensitivity using a simple electronics design. The developed system has been calibrated using a standard 3-L calibration syringe. The parameters of this system are validated against a standard spirometer, with maximum percentage error below 16%. Further, the extracted respiratory parameters related to tidal breathing have been found to be comparable with relevant prior works. The error in detecting respiration rate only is 3.9% compared to manual evaluation. These encouraging insights reveal the definite potential of our tidal breathing pattern (TBP) prototype for measuring tidal breathing parameters in order to extend the reach of affordable healthcare in rural regions and developing areas.

Artificial touch sensing system for various Human Computer Interaction (HCI) applications is required to be capable of recognizing various parameters viz. object shape, size, texture and surface. However, only identifying object-shapes is not sufficient for object recognition. It is necessary to distinguish the object shapes according to their dimensions or sizes. Thus in the present work object shapes as well as their sizes are recognized by processing and analysis of tactile images obtained by grasping different objects. In this study, statistical features are extracted from a number of acquired tactile images for classification in their respective object shape and size classes. Both inter-subject and intra-subject classifications are performed using four different classifiers (k-nearest neighbor (kNN), Naïve Bayes classifier, Linear Discriminant Analysis (LDA) and Ensemble) in one-versus-one (OVO) basis, which resulted in high classification accuracy independent of the type of classifier. The mean classification accuracies for inter-subject and intra-subject shape and size recognition are found to be 93%, 87% and 94% and 88% respectively.

Monitoring and detection of oedema severity is crucial for effective treatment of the underlying ailments. However, the conventional modalities for oedema detection either suffer from subjective assessment errors or involve setups that require harmful radiation, are expensive, tedious, and bulky. This work presents a novel, unobtrusive microwave-based sensing methodology utilizing the network analyzer, a customized antenna and associated RF components for oedema severity monitoring. The RF patch antenna in the present situation is customized for operation at 4 GHz under dielectric loading of the numerical human phantom. The performance of the customized antenna is compared with that of the corresponding standard patch antenna at 4 GHz to emphasize its significance for oedema detection. The proposed scheme is validated by establishing an experimental setup emulating realistic oedema scenarios, and a diverse dataset is built by measuring the reflection data at the designated frequency under various conditions. For oedema severity classification, a machine learning (ML) based approach is implemented making use of the extensive reflection data, and accordingly the physics-based features are extracted. Subsequently, by making use of the same, the ML assisted oedema severity (Low, Medium and High) classification, is carried out with a remarkable accuracy of 93.78±4.15%

Quantitative microwave imaging (MWI) has been an ongoing challenge in the scientific community due to the difficulties associated with the dielectric constant computation for biological tissues in the real scenario. This work explores a novel methodology for in-vivo, dielectric imaging of biological tissues by estimating their dielectric properties in the practical scenario using the free-space measurement setup. The proposed scheme utilizes a Vivaldi antenna, working in the 1-10 GHz range, to measure the reflection coefficient of the material under test (MUT) in the free space with metallic reflectors as references. The relative shift in the time-domain associated with various samples is then used to estimate the dielectric constant of the MUT. The applicability of the proposed scheme is first demonstrated using the simulation-based models for various standard samples. For biomedical applications, subcutaneous fat tissue is used as a MUT, and the water content is varied to emulate various abnormal conditions. Finally, the proposed scheme is experimentally validated by employing wood as a biological phantom, and the measurements are carried out by varying the water content in the reference sample. It is observed that the dielectric constant of samples can be computed quite accurately (with an error margin of < 7%). This indicates the competence of the proposed scheme for utilization in in-body dielectric imaging.