Alzheimer’s disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer’s patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer’s disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer’s. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer’s disease.

Tremor is a primary symptom of common movement disorders such as Essential tremor and Parkinson’s disease. People experiencing tremors face difficulty in performing everyday tasks which negatively impacts their independence. Tremor quantification helps clinicians in evaluating disease progression and treatment response. Majority of the existing methods include cameras or motion sensors embedded in wearables and hand-held devices. The continuous wearing of contact sensors can be uneasy for the patients while the cameras cause privacy concerns. This paper proposes a novel method for tremor quantification using a Frequency Modulated Continuous Wave (FMCW) radar sensor. In this paper, an off-the shelf, low-cost FMCW radar has been configured to capture vibrations induced in distal limbs, a representative feature of Essential and Parkinson’s Tremors. Moreover, a signal processing chain is developed to extract characteristic tremor frequency and amplitude by reconstructing the tremor signal from the radar return signals. For robustness and increased accuracy, static clutter and voluntary body motion are eliminated. Extensive experiments were performed and results were compared to the state-of-the-art methods that use accelerometers and gyroscopes. A strong correlation ( R ² >0.97) is found between the reference sensor readings and predicted values for both quantified parameters. The mean error for the frequency and amplitude is 0.14Hz and 0.03cm, respectively. Results demonstrate a superior accuracy as compared to the existing non-contact techniques, with the added advantage of privacy and integrity preserving for the end-user. Hence, the proposed system can provide reliable long-term objective assessment, aiding clinicians in the evaluation of tremor severity and treatment effectiveness.

Background: There is growing interest in developing sensing solutions for remote health monitoring to support the safety and independence of older adults. To ensure these technologies are practical and relevant, people-centred design is essential. This study aims to explore the involvement of various stakeholders across different developmental stages to inform the design and assess the capabilities of unobtrusive sensing solutions being developed as part of the Advanced Care Research Centre (ACRC), Edinburgh, UK. Methods: This study was conducted in two phases. In Phase I (Ideation), discussions were held with stakeholders (n = 19), including senior geriatricians (n = 2), healthcare and care home professionals (n = 4), PPI experts (n = 2), researchers (n = 4) and public members aged 65 and above from the ACRC Patient and Public Involvement (PPI) Network (n = 7). The goal was to identify clinically significant health parameters and design preferences. Based on this, prototypes of unobtrusive sensors for monitoring movement, hydration, and respiration were developed. In Phase II (Development and Co-Design), an in-person PPI workshop was conducted with PPI experts (n = 2), researchers (n = 4) and PPI members (n = 8). The developed prototypes were demonstrated, and qualitative feedback was collected through focus group discussions on themes such as acceptability, usability, privacy, data sharing, and functionality enhancement. Results: Stakeholder input from Phase I emphasized the importance of non-contact sensing technologies that maintain privacy. Movement, hydration, and respiration were identified as critical health parameters. In Phase II, PPI members were optimistic about the prototypes, valuing their unobtrusive design and privacy-preserving features. Key themes identified included (1) the need for user-customized alarms, (2) clear data-sharing protocols, and (3) the importance of embedding sensors into familiar household objects. Suggestions for refining the prototypes included adding functionality for detecting deviations in daily routines and integrating feedback mechanisms for caregivers. Conclusions: Involving diverse stakeholders from the early stages of technology development enhanced the relevance and acceptability of unobtrusive sensing solutions. This study highlights the importance of integrating public perspectives into the design process. For successful implementation, developers of healthcare technologies should prioritize privacy, usability, and clear communication with end-users and caregivers.

Accurate estimation of gait parameters depends on the prediction of key gait events of heel strike (HS) and toe-off (TO). Kinematics-based gait event estimation has shown potential in this regard, particularly using leg and foot velocity signals and gyroscopic sensors. However, existing algorithms demonstrate a varying degree of accuracy for different populations. Moreover, the literature lacks evidence for their validity for the amputee population. The purpose of this study is to evaluate this paradigm to predict TO and HS instants and to propose a new algorithm for gait parameter estimation for the amputee population. An open data set containing marker data of 12 subjects with unilateral transfemoral amputation during treadmill walking was used, containing around 3400 gait cycles. Five deterministic algorithms detecting the landmarks (maxima, minima, and zero-crossings [ZC]) in the foot, shank, and thigh angular velocity data indicating HS and TO events were implemented and their results compared against the reference data. Two algorithms based on foot and shank velocity minima performed exceptionally well for the HS prediction, with median accuracy in the range of 6–13 ms. However, both these algorithms produced inferior accuracy for the TO event with consistent early prediction. The peak in the thigh velocity produced the best result for the TO prediction with <25 ms median error. By combining the HS prediction using shank velocity and TO prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters (step, stride times, stance, and double support timings) with a median error of less than 25 ms. In conclusion, combined shank and thigh velocity-based prediction leads to improved gait parameter estimation than traditional algorithms for the amputee population.