Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload.

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

Embedded Bio-Mimetic System for Functional Electrical Stimulation Controlled by Event-Driven sEMG.

The analysis of the surface ElectroMyoGraphic (sEMG) signal for controlling the Functional Electrical Stimulation (FES) therapy is being widely accepted as an active rehabilitation technique for the restoration of neuro-muscular disorders. Portability and real-time functionalities are major concerns, and, among others, two correlated challenges are the development of an embedded system and the implementation of lightweight signal processing approaches. In this respect, the event-driven nature of the Average Threshold Crossing (ATC) technique, considering its high correlation with the muscle force and the sparsity of its representation, could be an optimal solution. In this paper we present an embedded ATC-FES control system equipped with a multi-platform software featuring an easy-to-use Graphical User Interface (GUI). The system has been first characterized and validated by analyzing CPU and memory usage in different operating conditions, as well as measuring the system latency (fulfilling the real-time requirements with a 140 ms FES definition process). We also confirmed system effectiveness, testing it on 11 healthy subjects: The similarity between the voluntary movement and the stimulate one has been evaluated, computing the cross-correlation coefficient between the angular signals acquired during the limbs motion. We obtained high correlation values of 0.87 ± 0.07 and 0.93 ± 0.02 for the elbow flexion and knee extension exercises, respectively, proving good stimulation application in real therapy-scenarios.

Assessing the Feasibility of Augmenting Fall Detection Systems by Relying on UWB-Based Position Tracking and a Home Robot.

Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand.

Wearable Heart Rate Monitoring Device Communicating in 5G ISM Band for IoHT.

Advances in wearable device technology pave the way for wireless health monitoring for medical and non-medical applications. In this work, we present a wearable heart rate monitoring platform communicating in the sub-6GHz 5G ISM band. The proposed device is composed of an Aluminium Nitride (AlN) piezoelectric sensor, a patch antenna, and a custom printed circuit board (PCB) for data acquisition and transmission. The experimental results show that the presented system can acquire heart rate together with diastolic and systolic duration, which are related to heart relaxation and contraction, respectively, from the posterior tibial artery. The overall system dimension is 20 mm by 40 mm, and the total weight is 20 g, making this device suitable for daily utilization. Furthermore, the system allows the simultaneous monitoring of multiple subjects, or a single patient from multiple body locations by using only one reader. The promising results demonstrate that the proposed system is applicable to the Internet of Healthcare Things (IoHT), and particularly Integrated Clinical Environment (ICE) applications.

Silicon Carbide-Gated Nanofluidic Membrane for Active Control of Electrokinetic Ionic Transport.

Manipulation of ions and molecules by external control at the nanoscale is highly relevant to biomedical applications. We report a biocompatible electrode-embedded nanofluidic channel membrane designed for electrofluidic applications such as ionic field-effect transistors for implantable drug-delivery systems. Our nanofluidic membrane includes a polysilicon electrode electrically isolated by amorphous silicon carbide (a-SiC). The nanochannel gating performance was experimentally investigated based on the current-voltage (I-V) characteristics, leakage current, and power consumption in potassium chloride (KCl) electrolyte. We observed significant modulation of ionic diffusive transport of both positively and negatively charged ions under physical confinement of nanochannels, with low power consumption. To study the physical mechanism associated with the gating performance, we performed electrochemical impedance spectroscopy. The results showed that the flat band voltage and density of states were significantly low. In light of its remarkable performance in terms of ionic modulation and low power consumption, this new biocompatible nanofluidic membrane could lead to a new class of silicon implantable nanofluidic systems for tunable drug delivery and personalized medicine.

On Integration and Validation of a Very Low Complexity ATC UWB System for Muscle Force Transmission.

The thresholding of Surface ElectroMyoGraphic (sEMG) signals, i.e., Average Threshold Crossing (ATC) technique, reduces the amount of data to be processed enabling circuit complexity reduction and low power consumption. This paper investigates the lowest level of complexity reachable by an ATC system through measurements and in-vivo experiments with an embedded prototype for wireless force transmission, based on asynchronous Impulse-Radio Ultra Wide Band (IR-UWB). The prototype is composed by the acquisition unit, a wearable PCB 23 × 34 mm, which includes a full custom IC integrating a UWB transmitter (chip active silicon area 0.016 mm(2), 1 mW power consumption), and the receiver. The system is completely asynchronous, it acquires a differential sEMG signal, generates the ATC events and triggers a 3.3 GHz IR-UWB transmission. ATC robustness relaxes filters constraints: two passive first order filters have been implemented, bandwidth from 10 Hz up to 1 kHz. Energy needed for the single pulse generation is 30 pJ while the whole PCB consumes 5.65 mW. The pulses radiated by the acquisition unit TX are received by a short-range and low complexity threshold-based 130 nm CMOS IR-UWB receiver with an Ultra Low Power (ULP) baseband unit capable of robustly receiving generic quasi-digital pulse sequences. The acquisition unit have been tested with 10 series of in vivo isometric and isotonic contractions, while the transmission channel with over-the-air and cable measurements obtained with a couple of planar monopole antennas and an integrated 0.004 mm(2) transmitter, the same used for the acquisition unit, with realistic channel conditions. The entire system, acquisition unit and receiver, consumes 15.49 mW.

A new dynamic tactile display for reconfigurable braille: implementation and tests.

Different tactile interfaces have been proposed to represent either text (braille) or, in a few cases, tactile large-area screens as replacements for visual displays. None of the implementations so far can be customized to match users' preferences, perceptual differences and skills. Optimal choices in these respects are still debated; we approach a solution by designing a flexible device allowing the user to choose key parameters of tactile transduction. We present here a new dynamic tactile display, a 8 × 8 matrix of plastic pins based on well-established and reliable piezoelectric technology to offer high resolution (pin gap 0.7mm) as well as tunable strength of the pins displacement, and refresh rate up to 50s(-1). It can reproduce arbitrary patterns, allowing it to serve the dual purpose of providing, depending on contingent user needs, tactile rendering of non-character information, and reconfigurable braille rendering. Given the relevance of the latter functionality for the expected average user, we considered testing braille encoding by volunteers a benchmark of primary importance. Tests were performed to assess the acceptance and usability with minimal training, and to check whether the offered flexibility was indeed perceived by the subject as an added value compared to conventional braille devices. Different mappings between braille dots and actual tactile pins were implemented to match user needs. Performances of eight experienced braille readers were defined as the fraction of correct identifications of rendered content. Different information contents were tested (median performance on random strings, words, sentences identification was about 75%, 85%, 98%, respectively, with a significant increase, p < 0.01), obtaining statistically significant improvements in performance during the tests (p < 0.05). Experimental results, together with qualitative ratings provided by the subjects, show a good acceptance and the effectiveness of the proposed solution.