Mild cognitive impairment (MCI) could be a transitory stage to Alzheimer’s disease (AD) and underlines the importance of early detection of this stage. In MCI stage, though the older adults are not completely dependent on others for day-to-day tasks, mild impairments are seen in memory, attention, etc., subtly affecting their daily activities/routines. Smart sensing technologies, such as wearable and non-wearable sensors, coupled with advanced predictive modeling techniques enable daily activities/routines based early detection of MCI symptoms. Non-wearable sensors are less intrusive and can monitor activities at naturalistic environment with no interference to an individual’s daily routines. This review seeks to answer the following questions: (1) What is the evidence for use of non-wearable sensor technologies in early detection of MCI/AD utilizing daily activity data in an unobtrusive manner? (2) How are the machine learning methods being employed in analyzing activity data in this early detection approach? A systematic search was conducted in databases such as IEEE Explorer, PubMed, Science Direct, and Google Scholar for the papers published from inception till March 2019. All studies that fulfilled the following criteria were examined: a research goal of detecting/predicting MCI/AD, daily activities data to detect MCI/AD, noninvasive/non-wearable sensors for monitoring activity patterns, and machine learning techniques to create the prediction models. Out of 2165 papers retrieved, 12 papers were eligible for inclusion in this review. This review found a diverse selection of aspects such as sensors, activity domains/features, activity recognition methods, and abnormality detection methods. There is no conclusive evidence on superiority of one or more of these aspects over the others, especially on the activity feature that would be the best indicator of cognitive decline. Though all these studies demonstrate technological developments in this field, they all suggest it is far in the future it becomes an effective diagnostic tool in real-life clinical practice.

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Nanorobotics is the technology of creating robots at nanoscale. Specifically, nanorobotics refers to the hypothetical nanotechnology engineering discipline of designing and building nanorobots, devices ranging in size from 0.1-10 micrometers and constructed of molecular components. On this concept of artificial non-biological nanorobots, many research centers are performing the research activities. The names nanobots, nanoids, nanites or nanomites have also been used to describe these hypothetical devices. They are applied in advanced medical applications like diagnosis and treatment of diabetes, early detection and treatment of cancer, cellular nonosurgery and genetherapy. A few generations from now someone diagnosed with cancer might be offered a new alternative to chemotherapy. A doctor practicing nanomedicine of chemotherapy would offer the patient an injection of a special type of nanorobot that would seek out cancer cells and destroy them, dispelling the disease at the source, leaving healthy cells untouched unlike the traditional treatment of radiation that kills not only cancer cells but also healthy human cells. Radiation treatment may also cause hair loss, fatigue, nausea, depression, and a host of other symptoms. Thus in nanorobotics, the extent of the hardship to the patient would essentially be a prick to the arm. A person undergoing a nanorobotic treatment could expect to have no awareness of the molecular devices working inside them, other than rapid betterment of their health. A major advantage that nanorobots provide is durability, as they could last for years. The operation time would also be much lower because their displacements are smaller. Hence reduced material costs, accessibility to previously unreachable areas are the motivating factors. Thus our review explains that the designing and testing of primitive devices and their potential applications promise rich benefits for patients, medical personal, engineers, and scientists.