Timothy G. Constandinou

Millimeter-wave radar can quietly monitor health and behavior at home, which is vital for supporting people living with dementia. Most studies, however, remain limited to short-term testing in controlled spaces. Real-world deployment requires robust activity classification as a prerequisite: vital-sign and behavioral sensing require fundamentally different processing pipelines, and absent periods need to be reliably distinguished from stationary states. Bridging the critical gap between controlled laboratory demonstrations and continuous home monitoring, this paper introduces a self-adapting radar framework that extracts meaningful behavioral segments from massive, unconstrained real-world data. The system performs continuous real-time activity classification (stationary, walking, and absent) and target localization, selectively directing downstream processing to the most informative segments. It addresses key real-world deployment challenges including adaptive thresholding across subjects and environments, and walking detection under naturalistic activity conditions. Prior to integration with the Minder platform, the system was validated in a fully instrumented studio apartment against ground truth. Across 12 subjects, the system achieved an overall classification accuracy of 0.98, with F1 scores of 0.99 for absence and stationary states, and 0.95 for walking. Event-based evaluation yielded a per-subject walking sensitivity of 0.916 ± 0.058 and F1 score of 0.935 ± 0.030. Localization root mean square error during movement was 0.40 m. The results demonstrate reliable performance suitable for transitioning to long-term real-world home deployment.

Disruption of sleep and circadian rhythms is a lesser-known, but pervasive symptom of Parkinson’s disease. It impacts patients’ health, well-being, and may exacerbate disease progression. This review summarises how currently established treatments impact Parkinsonian sleep and circadian rhythms, and highlights experimental treatments (chronotherapies) that specifically target these symptoms. We argue deep brain stimulation technology may have the potential to deliver particularly effective chronotherapy, and we explore several potential therapeutic strategies.

Emerging resistive switching devices require large-scale characterization, yet existing approaches often rely on external instrumentation, small custom arrays or architectures that lack autonomous current sensing. This work introduces MELITA, a fully integrated CMOS platform that supports parallel characterization of more than one million two-terminal devices. Fabricated in $\mathrm {180~ nm }$ CMOS, the chip combines on-chip DACs, current-to-voltage converters, column-parallel ADCs, and a digital controller into a unified readout architecture. Automated current ranging extends measurable input currents across five decades, while programmable read voltages enable flexible test conditions. Measured results confirm both block-level and system-level operation, with a 12 bit digital output. Application-specific experiments demonstrate the ability to extract I-V curves directly from large-scale two-terminal device arrays composed of passive top-metal structures, validating scalability and readout functionality of the platform. Although noise limits the effective Signal-to-Noise Ratio (SNR) to 43dB, the platform uniquely integrates array access, analog front-end, digitization, and control into a scalable test vehicle. MELITA therefore provides a practical step toward realistic benchmarking of resistive devices within system-level CMOS environments.

Objective. Peripheral nerve stimulation (PNS) offers therapeutic benefits across numerous clinical applications but remains limited by poor spatial selectivity in mixed nerves. This study aimed to evaluate whether a fully polymeric, transverse, multipolar nerve cuff can achieve selective fascicular activation and to assess the predictability of such selectivity using imaging-informed computational models. Approach. A flexible, fully polymeric nerve cuff fabricated from a conductive elastomer (CE) was developed and evaluated ex vivo on rat sciatic nerves. Compound nerve action potentials were recorded from individual fascicles to quantify selectivity across a wide range of stimulation parameters. The non-metallic electrodes enabled microCT-based three-dimensional reconstruction of nerve–electrode geometries without imaging artifacts, which were incorporated into anatomically accurate simulations using the ASCENT modeling pipeline. Main results. Ex vivo experiments demonstrated reliable neural recordings and high levels of fascicular selectivity (selectivity index > 0.65 in each fascicle). Imaging-informed simulations reproduced selective activation patterns in some cases but showed systematic discrepancies in both selectivity magnitude and electrode–fascicle correspondence, particularly for sural and tibial fascicles. Simulated outcomes were more sensitive to neuroanatomical variability than experimental results, highlighting limitations in current modeling assumptions. Significance. These findings validate fully polymeric CE cuffs as effective alternatives to metallic nerve interfaces for selective PNS. The study also demonstrates the value of combining microCT imaging with computational modeling to interrogate and refine predictive frameworks, underscoring the need for improved tissue and electrode modeling to advance spatially selective PNS technologies.

Gait impairment is a hallmark symptom of Parkinson’s disease (PD). However, traditional clinical assessments cannot capture real-world motor fluctuations, as they are sparsely performed. This study was designed to test and validate the use of nearables and passive sensing technologies, including Kinect RGB-D cameras and ultra-wideband (UWB) radar, for continuous, objective assessment of gait fluctuations in PD within a home-like setting. Fifteen PD patients with mild symptoms and fourteen age-and sex-matched healthy controls (HC) performed 4-meter walking tasks in a living lab facility. Patients repeated the task during both “ON” and “OFF” states of their daily medication cycle. Gait features, including stride length, stride time, and gait speed, were extracted from Kinect, radar, and a ground-truth smart floor. Data were analyzed to evaluate inter-sensor agreements and detect group-level differences. Stride time demonstrated the highest agreement between devices ( r =0.903), while stride length showed weaker agreement ( r =0.779), with Kinect tending to overestimate. Despite lower agreement, stride length from both Kinect and radar successfully distinguished PD OFF from HC (camera q =0.020; radar q =0.005) and radar was able to further differentiate ON and OFF states ( q =0.020). Neither device differentiated PD ON from HC, indicating medication reduced observable gait differences. This study demonstrates that passive, contact-free sensing technologies such as depth cameras and UWB radars can effectively monitor gait in PD within naturalistic environments. While some spatial metrics, like stride length, show device discrepancies, both systems reliably capture gait patterns and medication-dependent changes, supporting their use for longitudinal, real-world monitoring of Parkinson’s motor symptoms.

Peripheral nerve stimulation (PNS) offers therapeutic benefits across numerous clinical applications but suffers from limitations in high resolution spatial selectivity, especially in mixed nerves. This study presents a fully polymeric, transverse, multipolar nerve cuff made from a conductive elastomer (CE), designed for selective activation of individual fascicles. Fabricated with laser-based manufacturing techniques, the CE nerve cuff offers mechanical conformity and high charge injection capacity. Ex vivo experiments on the rat sciatic nerve demonstrate reliable compound action potential recordings and fascicular selectivity (SI > 0.65). The non-metal electrodes enable microCT-aided 3D reconstruction of nerve-electrode geometries without imaging artefacts, informing anatomically accurate simulations via the ASCENT pipeline. While in silico simulations predict some selective fascicular activation, discrepancies were observed between predicted and experimental selectivity magnitudes and electrode positions, particularly for the sural and tibial fascicles. The model was more sensitive to neuroanatomical variation than the experimental data, indicating limitations in current perineurium and CE electrode modelling assumptions. This work validates CE-based cuffs as viable alternatives to metallic devices for selective fascicular peripheral nerve activation and highlights the potential of imaging-informed simulations to optimize nerve interface design. Future improvements in electrode and nerve tissue modelling are needed to enhance in silico prediction accuracy and further advance spatially selective PNS technologies.

This paper presents an adaptable RO-TRNG that removes the fixed power–throughput tradeoff by selecting delay-cell physics at run time. A hybrid core uses a current-starved inverter in low-power (LP) mode to amplify slew-limited jitter for high bit-efficiency at low frequency, and a latched cross-coupled cell in high-performance (HP) mode to exploit regeneration-time jitter over a wider band; both share a unified fast-by-slow sampling path with XOR combining. Fabricated in 180 nm CMOS (265×490 μm), the TRNG spans 0.8–2.0 V and -20–80 ∘C, achieves 168 nW (3.95 pJ/bit) in LP and 44.3 Mbps in HP, and reaches near-ideal HP entropy (0.999999999984). Long datasets pass NIST SP 800-22 (including under 400 mV injection at the second harmonic), SP 800-90B, and AIS31. A single, digitally-tunable IP thus delivers nanowatt standby entropy and burst-mode throughput without architectural change.

The reliability of polymer-encapsulated CMOS integrated circuits (ICs) is critical for the development of miniaturised active implantable medical devices (AIMDs). Traditional hermetic packaging methods become impractical as implant sizes decrease, necessitating alternative methods of protection from body fluids. This study evaluates the long-term stability of silicone-encapsulated CMOS ICs through accelerated life testing using electrical impedance spectroscopy (EIS) and visual inspection. CMOS interdigitated combs (IDCs) were encapsulated in medical grade silicone rubber, subjected to immersion in phosphate-buffered saline (PBS), and tested at elevated temperatures (47°C, 67°C, and 87°C) under both 5V DC and biphasic voltage biases for up to 4.3 years. Remarkably, no insulation failures were observed in the IDCs, with no significant water ingress detected through impedance changes. Failures at the ICs were limited to wire bond open-circuits, though there was some pad discolouration/corrosion. Other failures were elsewhere, not at the ICs. This highlights the stability of modern silicon oxide/silicon nitride bilayer passivation when encapsulated in adhesive silicone rubber. Visual analysis revealed occasional solder and aluminium pad corrosion, particularly at higher temperatures, but these changes did not correlate with EIS failures. The findings suggest that silicone encapsulation, combined with passivation and shielding strategies, enables long-term IC reliability in biofluid environments. To our knowledge, this paper presents the longest reported accelerated ageing study of test structures for implantable devices, laying the groundwork for the integration of silicone-encapsulated ICs into next-generation chip-scale bioelectronic implants.