With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old‐age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.

Modular robots are capable of forming primitive shapes such as lattice and chain structures with the additional flexibility of distributed sensing. The biomimetic structures developed using such modular units provides ease of replacement and reconfiguration in co-ordinated structures, transportation etc. in real life scenarios. Though the research in the employment of modular robotic units in formation of biological organisms is in the nascent stage, modular robotic units are already capable of forming such sophisticated structures. The modular robotic designs proposed so far in modular robotics research vary significantly in external structures, sensor-actuator mechanisms interfaces for docking and undocking, techniques for providing mobility, coordinated structures, locomotions etc. and each robotic design attempted to address various challenges faced in the domain of modular robotics by employing different strategies. This paper presents a novel modular wheeled robotic design - HexaMob facilitating four degrees of freedom (2 degrees for mobility and 2 degrees for structural reconfiguration) on a single module with minimal usage of sensor-actuator assemblies. The crucial features of modular robotics such as back-driving restriction, docking, and navigation are addressed in the process of HexaMob design. The proposed docking mechanism is enabled using vision sensor, enhancing the capabilities in docking as well as navigation in co-ordinated structures such as humanoid robots.

With the advent of internet of things (IoT), new network paradigms have emerged. One such technology is cloudlets. Cloudlets are being increasingly used in various IoT-based applications such as smart homes, smart cities, healthcare, and industrial automations. Cloudlets have an advantage of proximity to the end-device while offering services similar to the cloud. Existing cloudlets use IEEE 802.11 for communication between nodes. In this paper, the authors present a protocol customized for usage in cloudlets, which also considers various limitations of the node that constitute the cloudlet. The nodes on the cloudlet are generally constrained in terms of power and memory when compared to nodes on a cloud. The custom protocol also incorporates fault-tolerance, time synchronization, and factors such as task affinities for communication. The protocol proposed in this paper gave an excellent packet delivery ratio, the lowest being 91% even with increased bandwidth usage when compared to IEEE 802.11.

This paper describes a dataset acquired from 41 volunteers performing 16 Activities of daily livings (ADLs) and 8 Falls repeated 5 times. This data was collected using a custom wrist-worn end device. The dataset has data collected from Inertial measurement unit (IMU) and heart-rate sensors. The end device is built using Qualcomm Snapdragon 820c System on Chip (SoC) interfaced to the sensors via Interconnect Integrated Circuit (I2C) protocol. The data was sampled for every activity at a rate of 20 Hz for the motion sensors and at a rate of 1 Hz for the heart-rate sensor. The motion sensor comprised of a triaxial accelerometer, triaxial gyroscope, triaxial magnetometer and a linear accelerometer. The heart-rate sensor was medical grade and all sensors were calibrated for the wrist -worn position. The dataset is available on this website https://shamanx86.github.io/fall_detection_data/ and https://doi.org/10.5281/zenodo.10013090.

The planning of flight operations and maintenance is a crucial activity for both commercial and military aircraft. Military aircraft have to be always mission-ready. The task of ensuring this can become quite challenging when several operational requirements and maintenance constraints are to be fulfilled simultaneously. This paper, therefore, addresses the optimisation of flight and maintenance planning (FMP) when several diverse factors such as aircraft flying hours (AFH), flight cycles (FC), calendar life, annual flying requirement (AFR), etc. are to be factored in. Such a problem has not been considered previously. Because the problem can become unwieldy to solve by other methods, two schemes, that is, the genetic algorithm (GA) and modified artificial bee colony (ABC) algorithm for constrained optimisation have been utilised. The objective is to maximise the utilisation rate (UR) of aircraft, while also satisfying other operational and maintenance constraints. The algorithm is tested on a fleet of eight aircraft. In addition to a one-year planning period, a planning horizon of ten years has also been simulated. The results show that both the GA and modified ABC algorithm can be effectively used to solve the FMP problem.