Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition.

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach's ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification.

ML-Based Edge Node for Monitoring Peoples' Frailty Status.

The development of contactless methods to assess the degree of personal hygiene in elderly people is crucial for detecting frailty and providing early intervention to prevent complete loss of autonomy, cognitive impairment, and hospitalisation. The unobtrusive nature of the technology is essential in the context of maintaining good quality of life. The use of cameras and edge computing with sensors provides a way of monitoring subjects without interrupting their normal routines, and has the advantages of local data processing and improved privacy. This work describes the development an intelligent system that takes the RGB frames of a video as input to classify the occurrence of brushing teeth, washing hands, and fixing hair. No action activity is considered. The RGB frames are first processed by two Mediapipe algorithms to extract body keypoints related to the pose and hands, which represent the features to be classified. The optimal feature extractor results from the most complex Mediapipe pose estimator combined with the most complex hand keypoint regressor, which achieves the best performance even when operating at one frame per second. The final classifier is a Light Gradient Boosting Machine classifier that achieves more than 94% weighted F1-score under conditions of one frame per second and observation times of seven seconds or more. When the observation window is enlarged to ten seconds, the F1-scores for each class oscillate between 94.66% and 96.35%.

Intelligent Human-Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition.

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human-computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios.

Acquisition Devices for Fetal Phonocardiography: A Scoping Review.

Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its noninvasiveness, the technique is suitable for long-term monitoring and for telemonitoring applications. Despite the high share of literature focusing on signal processing, no previous work has reviewed the technological hardware solutions devoted to the recording of fetal heart sounds. Thus, the aim of this scoping review is to collect information regarding the acquisition devices for fetal phonocardiography (FPCG), focusing on technical specifications and clinical use. Overall, PRISMA-guidelines-based analysis selected 57 studies that described 26 research prototypes and eight commercial devices for FPCG acquisition. Results of our review study reveal that no commercial devices were designed for fetal-specific purposes, that the latest advances involve the use of multiple microphones and sensors, and that no quantitative validation was usually performed. By highlighting the past and future trends and the most relevant innovations from both a technical and clinical perspective, this review will represent a useful reference for the evaluation of different acquisition devices and for the development of new FPCG-based systems for fetal monitoring.

Physiological and Biomechanical Monitoring in American Football Players: A Scoping Review.

American football is the sport with the highest rates of concussion injuries. Biomedical engineering applications may support athletes in monitoring their injuries, evaluating the effectiveness of their equipment, and leading industrial research in this sport. This literature review aims to report on the applications of biomedical engineering research in American football, highlighting the main trends and gaps. The review followed the PRISMA guidelines and gathered a total of 1629 records from PubMed (n = 368), Web of Science (n = 665), and Scopus (n = 596). The records were analyzed, tabulated, and clustered in topics. In total, 112 studies were selected and divided by topic in the biomechanics of concussion (n = 55), biomechanics of footwear (n = 6), biomechanics of sport-related movements (n = 6), the aerodynamics of football and catch (n = 3), injury prediction (n = 8), heat monitoring of physiological parameters (n = 8), and monitoring of the training load (n = 25). The safety of players has fueled most of the research that has led to innovations in helmet and footwear design, as well as improvements in the understanding and prevention of injuries and heat monitoring. The other important motivator for research is the improvement of performance, which has led to the monitoring of training loads and catches, and studies on the aerodynamics of football. The main gaps found in the literature were regarding the monitoring of internal loads and the innovation of shoulder pads.

Identification of Respiration Types Through Respiratory Signal Derived From Clinical and Wearable Electrocardiograms.

GOAL: To evaluate suitability of respiratory signals derived from clinical 12-lead electrocardiograms (ECGs) and wearable 1-lead ECG to identify different respiration types. METHODS: ECGs were simultaneously acquired through the M12R ECG Holter by Global Instrumentation and the chest strap BioHarness 3.0 by Zephyr from 42 healthy subjects alternating normal breathing, breath holding, and deep breathing. Respiration signals were derived from the ECGs through the Segmented-Beat Modulation Method (SBMM)-based algorithm and the algorithms by Van Gent, Charlton, Soni and Sarkar, and characterized in terms of breathing rate and amplitude. Respiration classification was performed through a linear support vector machine and evaluated by F1 score. RESULTS: Best F1 scores were 86.59%(lead V2) and 80.57%, when considering 12-lead and 1-lead ECGs, respectively, and using SBMM-based algorithm. CONCLUSION: ECG-derived respiratory signals allow reliable identification of different respiration types even when acquired through wearable sensors, if associated to appropriate processing algorithms, such as the SBMM-based algorithm.

Automatic Optical Measurement and Control of Benzene and Benzenoids in Natural Gas Pipelines.

The presence of benzene and similar aromatic compounds in civil environments is due to anthropic actions but also to natural sources. Natural gas consists of a gas mixture where benzene and related compounds are usually presents. Thus, the detection of these compounds in natural gas pipelines is of the utmost importance as well as the control of the concentration level, which must remain below the limits consented by law. In this regard, it is of striking interest to engineer devices able to detect these compounds by automatic and continuous remote control. Here, we discuss the application of an optical device designed for the measurement of sulfured odorizing agents in natural gas pipelines aiming at the detection and the measurement of benzene, toluene, and xylenes (BTX) in the same contexts. The instrument consists of a customized UV spectrophotometer connected to an automatic control system able to provide in-field detections of BTX through a continuous and remote check of the gaseous mixture. Relatively to benzene, the instrument is characterized by values of LOD (level of detection) and LOQ (level of quantification) equal to 0.55 and 1.84 mg/Sm3, respectively. Similar limits are found for toluene and xylenes (LOD of 0.81, 1.05, 1.41, and 1.00 mg/Sm3 for toluene, meta-, ortho-, and para-xylene, respectively).

Odorant Monitoring in Natural Gas Pipelines Using Ultraviolet-Visible Spectroscopy.

The remote, timely and in-field detection of sulfured additives in natural gas pipelines is a challenge for environmental, commercial and safety reasons. Moreover, the constant control of the level of odorants in a pipeline is required by law to prevent explosions and accidents. Currently, the detection of the most common odorants (THT = tetrahydrothiophane; TBM = tertiary butyl mercaptan) added to natural gas streams in pipelines is made in situ by using portable gas chromatography apparatuses. In this study, we report the analysis of the ultraviolet spectra obtained by a customized ultraviolet spectrophotometer, named Spectra, for the in-field detection of thiophane and tertiary butyl mercaptan. Spectra were conceived to accomplish the remote analysis of odorants in the pipelines of the natural gas stream through the adoption of technical solutions aimed to adapt a basic bench ultraviolet spectrophotometer to the in-field analysis of gases. The remotely controlled system acquires spectra continuously, performing the quantitative determination of odorants and catching systemic or accidental variations of the gaseous mixture in different sites of the pipeline. The analysis of the experimental spectra was carried out also through theoretical quantum mechanical approaches aimed to detect and to correctly assign the nature of the intrinsic electronic transitions of the two odorants, thiophane and tertiary butyl mercaptan, that cause the ultraviolet absorptions. So far, these theoretical aspects have never been studied before. The absorption maxima of thiophane and tertiary butyl mercaptan spectra were computationally simulated through the usage of selected molecular models with satisfactory results. The good matches between the experimental and theoretical datasets corroborate the reliability of the collected data. During the tests, unexpected pollutants and accidental malfunctions have been detected and also identified by Spectra, making this instrument suitable for many purposes.

Millimeter wave radar data of people walking.

This dataset contains complex signals coming from a mmWave FMCW radar system. Signals were acquired during a measurement campaign taken indoor and aimed to assess people's different ways of walking. Measurement setup and devices are described. The dataset consists of the acquisitions of six different types of activities, performed by 29 subjects who repeat each activity several times. Therefore, the dataset contains multiple different experiments for each activity, for a total of 231 acquisitions. The subjects walk without any constraint or do not follow any pattern, thus making this dataset useful not only for human gait recognition but also to evaluate the performance of different radar data processing algorithms.

Food Intake Actions Detection: An Improved Algorithm Toward Real-Time Analysis.

With the increase in life expectancy, one of the most important topic for scientific research, especially for the elderly, is good nutrition. In particular, with an advanced age and health issues because disorders such as Alzheimer and dementia, monitoring the subjects' dietary habits to avoid excessive or poor nutrition is a critical role. Starting from an application aiming to monitor the food intake actions of people during a meal, already shown in a previously published paper, the present work describes some improvements that are able to make the application work in real time. The considered solution exploits the Kinect v1 device that can be installed on the ceiling, in a top-down view in an effort to preserve privacy of the subjects. The food intake actions are estimated from the analysis of depth frames. The innovations introduced in this document are related to the automatic identification of the initial and final frame for the detection of food intake actions, and to the strong revision of the procedure to identify food intake actions with respect to the original work, in order to optimize the performance of the algorithm. Evaluation of the computational effort and system performance compared to the previous version of the application has demonstrated a possible real-time applicability of the solution presented in this document.

Simultaneously acquired data from contactless and wearable devices for direct and indirect heart-rate measurement.

The proposed dataset provides a complete set of simultaneously acquired data from contactless and wearable devices for direct and indirect heart-rate measurement. Data were acquired on a total of 20 healthy white Caucasian subjects wearing no makeup (10 males and 10 females; age: 22.50 ± 1.57 years; height: 173 ± 10 cm; weight: 62.80 ± 9.52 kg) and consisted of: i) videos of the subject's face acquired by a RGB-D (Red, Green, Blue and Depth) camera (Microsoft Kinect v2), which is a contactless device; ii) electrocardiographic (ECG) recordings acquired by a clinical Holter ECG recorder (Global Instrumentation's M12R Holter), which is a wearable device; and iii) heart-rate measurements acquired from a commercial smartwatch (Moto 360 smartwatch by Motorola), which is also a wearable device. ECG recordings were processed to extract the R-peaks position and obtain a reference indirect measurement of the heart rate. A direct measurement of the heart rate was provided by the commercial smartwatch. The dataset here presented could be useful to develop new algorithms for heart-rate detection from contactless devices and to validate contactless heart-rate estimation in comparison to reference heart rate from clinical wearable devices and to heart rate from commercial wearable devices.

Accurate Fall Detection in a Top View Privacy Preserving Configuration.

Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive.

Heart Rate Detection Using Microsoft Kinect: Validation and Comparison to Wearable Devices.

Contactless detection is one of the new frontiers of technological innovation in the field of healthcare, enabling unobtrusive measurements of biomedical parameters. Compared to conventional methods for Heart Rate (HR) detection that employ expensive and/or uncomfortable devices, such as the Electrocardiograph (ECG) or pulse oximeter, contactless HR detection offers fast and continuous monitoring of heart activities and provides support for clinical analysis without the need for the user to wear a device. This paper presents a validation study for a contactless HR estimation method exploiting RGB (Red, Green, Blue) data from a Microsoft Kinect v2 device. This method, based on Eulerian Video Magnification (EVM), Photoplethysmography (PPG) and Videoplethysmography (VPG), can achieve performance comparable to classical approaches exploiting wearable systems, under specific test conditions. The output given by a Holter, which represents the gold-standard device used in the test for ECG extraction, is considered as the ground-truth, while a comparison with a commercial smartwatch is also included. The validation process is conducted with two modalities that differ for the availability of a priori knowledge about the subjects' normal HR. The two test modalities provide different results. In particular, the HR estimation differs from the ground-truth by 2% when the knowledge about the subject's lifestyle and his/her HR is considered and by 3.4% if no information about the person is taken into account.

Analysis and Tools for Improved Management of Connectionless and Connection-Oriented BLE Devices Coexistence.

With the introduction of low-power wireless technologies, like Bluetooth Low Energy (BLE), new applications are approaching the home automation, healthcare, fitness, automotive and consumer electronics markets. BLE devices are designed to maximize the battery life, i.e., to run for long time on a single coin-cell battery. In typical application scenarios of home automation and Ambient Assisted Living (AAL), the sensors that monitor relatively unpredictable and rare events should coexist with other sensors that continuously communicate health or environmental parameter measurements. The former usually work in connectionless mode, acting as advertisers, while the latter need a persistent connection, acting as slave nodes. The coexistence of connectionless and connection-oriented networks, that share the same central node, can be required to reduce the number of handling devices, thus keeping the network complexity low and limiting the packet's traffic congestion. In this paper, the medium access management, operated by the central node, has been modeled, focusing on the scheduling procedure in both connectionless and connection-oriented communication. The models have been merged to provide a tool supporting the configuration design of BLE devices, during the network design phase that precedes the real implementation. The results highlight the suitability of the proposed tool: the ability to set the device parameters to allow us to keep a practical discovery latency for event-driven sensors and avoid undesired overlaps between scheduled scanning and connection phases due to bad management performed by the central node.

A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

Kinect as a tool for gait analysis: validation of a real-time joint extraction algorithm working in side view.

The Microsoft Kinect sensor has gained attention as a tool for gait analysis for several years. Despite the many advantages the sensor provides, however, the lack of a native capability to extract joints from the side view of a human body still limits the adoption of the device to a number of relevant applications. This paper presents an algorithm to locate and estimate the trajectories of up to six joints extracted from the side depth view of a human body captured by the Kinect device. The algorithm is then applied to extract data that can be exploited to provide an objective score for the "Get Up and Go Test", which is typically adopted for gait analysis in rehabilitation fields. Starting from the depth-data stream provided by the Microsoft Kinect sensor, the proposed algorithm relies on anthropometric models only, to locate and identify the positions of the joints. Differently from machine learning approaches, this solution avoids complex computations, which usually require significant resources. The reliability of the information about the joint position output by the algorithm is evaluated by comparison to a marker-based system. Tests show that the trajectories extracted by the proposed algorithm adhere to the reference curves better than the ones obtained from the skeleton generated by the native applications provided within the Microsoft Kinect (Microsoft Corporation, Redmond,WA, USA, 2013) and OpenNI (OpenNI organization, Tel Aviv, Israel, 2013) Software Development Kits.

Validation of an optimized algorithm to use Kinect in a non-structured environment for Sit-to-Stand analysis.

The aim of this work is to obtain reliable kinematic measures relative to the execution of the Sit-to-Stand functional evaluation test, by low-cost and widely diffused instrumentation, that even non-experienced users can adopt in non-structured environments, like ambulatory or domestic settings. In particular, the paper refers to a low cost RGB-Depth sensor widely used in the gaming scenario like the Microsoft Kinect sensor. An algorithm is proposed that allows a reliable measure of human motion in a sagittal view. The performance of the proposed algorithm is compared to other two classic commercial algorithms. Results obtained by all the three algorithms have been compared to kinematic results obtained by the use of a stereophotogrammetric system that represents the gold-standard for kinematic measurement of human movement. Average errors of about 4 degrees, both for the trunk/leg angle and for the knee flexion/extension angle, have been obtained by the proposed algorithm and open the way to its possible adoption in non-clinical environments and further applications.

A depth-based fall detection system using a Kinect® sensor.

We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an "on-ceiling" configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.

Remote health monitoring for elderly through interactive television.

BACKGROUND: Providing remote health monitoring to specific groups of patients represents an issue of great relevance for the national health systems, because of the costs related to moving health operators, the time spent to reach remote sites, and the high number of people needing health assistance. At the same time, some assistance activities, like those related to chronical diseases, may be satisfied through a remote interaction with the patient, without a direct medical examination. METHODS: Moving from this considerations, our paper proposes a system architecture for the provisioning of remote health assistance to older adults, based on a blind management of a network of wireless medical devices, and an interactive TV Set Top Box for accessing health related data. The selection of TV as the interface between the user and the system is specifically targeted to older adults. Due to the private nature of the information exchanged, a certified procedure is implemented for data delivery, through the use of non conditional smart cards. All these functions may be accomplished through a proper design of the system management, and a suitable interactive application. RESULTS: The interactive application acting as the interface between the user and the system on the TV monitor has been evaluated able to help readability and clear understanding of the contents and functions proposed. Thanks to the limited amount of data to transfer, even a Set Top Box equipped with a traditional PSTN modem may be used to support the proposed service at a basic level; more advanced features, like audio/video connection, may be activated if the Set Top Box enables a broadband connection (e.g. ADSL). CONCLUSIONS: The proposed layered architecture for a remote health monitoring system can be tailored to address a wide range of needs, according with each patient's conditions and capabilities. The system exploits the potentialities offered by Digital Television receivers, a friendly MHP interface, and the familiar remote control, to make the service effective and easy to use also for elderly people.

Technological approaches to remote monitoring of elderly people in cardiology: a usability perspective.

Moving from the experience gained in home telemonitoring of elderly patients with Congestive Heart Failure, that confirmed a reduction of the rehospitalization rate and an improved monitoring of drugs assumption by the patients, this paper extends the evaluation of technological approaches for remote health monitoring of older adults. Focus of the evaluation is on telemedicine effectiveness and usability, either from a patient's or a medical operator's perspective. The evaluation has been performed by testing three remote health platforms designed according to different technological approaches, in a realistic scenario involving older adults and medical operators (doctors and nurses). The aim of the testing activity was not to benchmark a specific solution with respect to the others, but to evaluate the main positive and negative issues related to the system and service design philosophy each solution was built upon. Though preliminary, the results discussed in the paper can be used as a set of guidelines in the selection of proper technological equipments for services targeted to elderly users, from a usability perspective. These results need to be complemented with more focused discussions of the ethical, medical, and legal aspects of the use of technology in remote healthcare.