Lorenzo Biotti

Bio: Lorenzo Biotti is an academic researcher from University of Florence. The author has contributed to research in topics: Visible light communication & Node (networking). The author has an hindex of 1, co-authored 3 publications receiving 2 citations.

Ultra-Wideband Radar-Based Indoor Activity Monitoring for Elderly Care.

Abstract: In this paper, we propose an unobtrusive method and architecture for monitoring a person’s presence and collecting his/her health-related parameters simultaneously in a home environment. The system is based on using a single ultra-wideband (UWB) impulse-radar as a sensing device. Using UWB radars, we aim to recognize a person and some preselected movements without camera-type monitoring. Via the experimental work, we have also demonstrated that, by using a UWB signal, it is possible to detect small chest movements remotely to recognize coughing, for example. In addition, based on statistical data analysis, a person’s posture in a room can be recognized in a steady situation. In addition, we implemented a machine learning technique (k-nearest neighbour) to automatically classify a static posture using UWB radar data. Skewness, kurtosis and received power are used in posture classification during the postprocessing. The classification accuracy achieved is more than 99%. In this paper, we also present reliability and fault tolerance analyses for three kinds of UWB radar network architectures to point out the weakest item in the installation. This information is highly important in the system’s implementation.

An Inertial Measurement Unit-Based Wireless System for Shoulder Motion Assessment in Patients with Cervical Spinal Cord Injury: A Validation Pilot Study in a Clinical Setting.

Abstract: Residual motion of upper limbs in individuals who experienced cervical spinal cord injury (CSCI) is vital to achieve functional independence Several interventions were developed to restore shoulder range of motion (ROM) in CSCI patients However, shoulder ROM assessment in clinical practice is commonly limited to use of a simple goniometer Conventional goniometric measurements are operator-dependent and require significant time and effort Therefore, innovative technology for supporting medical personnel in objectively and reliably measuring the efficacy of treatments for shoulder ROM in CSCI patients would be extremely desirable This study evaluated the validity of a customized wireless wearable sensors (Inertial Measurement Units-IMUs) system for shoulder ROM assessment in CSCI patients in clinical setting Eight CSCI patients and eight healthy controls performed four shoulder movements (forward flexion, abduction, and internal and external rotation) with dominant arm Every movement was evaluated with a goniometer by different testers and with the IMU system at the same time Validity was evaluated by comparing IMUs and goniometer measurements using Intraclass Correlation Coefficient (ICC) and Limits of Agreement (LOA) inter-tester reliability of IMUs and goniometer measurements was also investigated Preliminary results provide essential information on the accuracy of the proposed wireless wearable sensors system in acquiring objective measurements of the shoulder movements in CSCI patients

Bio-Optical Communication: a case-study of Out-to-In Body Interface

Abstract: Internet of bio nano things (IoBNT) is a recent concept which foresees the possibility to interconnect biological or artificial nano devices to the Internet. This would enable the inner part of the human body as part of the global network. One of the major challenge to provide this inter-connectivity is how to move the information from outside to inside the body. This paper proposes a bio-optical communication (BOC) as a potential solution. The paper proposes to use a visible light communication (VLC) LED-based lamp to send a message to the human brain using the eye retina as a relay node. In fact, the eye and the brain can be seen as a natural VLC receiver. We aim to demonstrate that a VLC signal with specific configuration parameters (frequency, etc.) can be successfully demodulated at the brain level and to provide analytically the channel capacity of the communication link.

Improved Extreme Learning Machine Based UWB Positioning for Mobile Robots with Signal Interference

Abstract: For the purpose of tackling ultra-wideband (UWB) indoor positioning with signal interference, a binary classifier for signal interference discrimination and positioning errors compensation model combining genetic algorithm (GA) and extreme learning machine (ELM) are put forward. Based on the distances between four anchors and the target which are calculated with time of flight (TOF) ranging technique, GA-ELM-based binary classifier for judging the existence of signal interference, and GA-ELM-based positioning errors compensation model are built up to compensate for the result of the preliminary evaluated positioning model. Finally, the datasets collected in the actual scenario are used for verification and analysis. The experimental results indicate that the root-mean-square error (RMSE) of positioning without signal interference is 14.5068 cm, which is reduced by 71.32% and 59.72% compared with those results free of compensation and optimization, respectively. Moreover, the RMSE of positioning with signal interference is 28.0861 cm, which is decreased by 64.38% and 70.16%, in comparison to their counterparts without compensation and optimization, respectively. Consequently, these calculated results of numerical examples lead to the conclusion that the proposed method displays its wide application, high precision and rapid convergence in improving the positioning accuracy for mobile robots.

Emerging Technologies for Next Generation Remote Health Care and Assisted Living

Abstract: Remote health care is currently one of the most promising solutions to ensure a high level of treatment outcome, cost-efficiency and sustainability of the healthcare systems worldwide. Even though research on remote health care can be traced back to the early days of the Internet, the recent COVID-19 has necessitated further improvement in existing health care systems with invigorated research on remote health care technologies. In this article we delve into the state-of-the-art research in latest technologies and technological paradigms that play a vital role in enabling the next generation remote health care and assisted living. First the need of using the latest technological developments in the domain of remote health care is briefly discussed. Then the most important technologies and technological paradigms that are crucial in enabling remote health care and assisted living are emphasised. Henceforth, a detailed survey of existing technologies, potential challenges in those technologies, and possible solutions is conducted. Finally, missing research gaps and important future research directions in each enabling technology are brought forth to motivate further research in remote health care.

Advanced User Interfaces for Teleoperated Surgical Robotic Systems

Abstract: In recent years, advances in modern technology have altered the practice of surgery from open to minimally invasive surgery (MIS) aided by robots. Teleoperated surgical robotic systems (TSRSs) provide numerous significant benefits for MIS over traditional approaches, including improved safety, more efficient and precise surgery, better cosmesis, shorter recovery time, and reduced postoperative pain. Existing TSRSs’ master consoles, with improvements in vision systems, designs, and control methods, have significantly enhanced human–robot interactions, resulting in safer and more accurate medical intervention operations. Despite advances, haptic technologies, including sensors, machine assistance, and intuitive devices for user interfaces, are still limited, resulting in less effective usage of TSRSs for surgical operations. This review presents a summary of the emerging TSRSs with a focus on their user interfaces. In addition, advanced sensing, haptic, smart garments, and medical image artificial intelligence (AI) assistance technologies are shown with their potential for use in master consoles of the TSRSs are shown. Finally, a discussion on the need for a smart human‐robot interface for TSRSs is given.

Wise Information Technology of Med: Human Pose Recognition in Elderly Care

Abstract: The growing problem of aging has led to a social concern on how to take care of the elderly living alone. Many traditional methods based on visual cameras have been used in elder monitoring. However, these methods are difficult to be applied in daily life, limited by high storage space with the camera, low-speed information processing, sensitivity to lighting, the blind area in vision, and the possibility of revealing privacy. Therefore, wise information technology of the Med System based on the micro-Doppler effect and Ultra Wide Band (UWB) radar for human pose recognition in the elderly living alone is proposed to effectively identify and classify the human poses in static and moving conditions. In recognition processing, an improved PCA-LSTM approach is proposed by combing with the Principal Component Analysis (PCA) and Long Short Term Memory (LSTM) to integrate the micro-Doppler features and time sequence of the human body to classify and recognize the human postures. Moreover, the classification accuracy with different kernel functions in the Support Vector Machine (SVM) is also studied. In the real experiment, there are two healthy men and one woman (22-26 years old) selected to imitate the movements of the elderly and slowly perform five postures (from sitting to standing, from standing to sitting, walking in place, falling and boxing). The experimental results show that the resolution of the entire system for the five actions reaches 99.1% in the case of using Gaussian kernel function, so the proposed method is effective and the Gaussian kernel function is suitable for human pose recognition.