Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning

Walking speed estimation is an essential component of mobile apps in various fields such as fitness, transportation, navigation, and health-care. Most existing solutions are focused on specialized medical applications that utilize body-worn motion sensors. These approaches do not serve effectively the general use case of numerous apps where the user holding a smartphone tries to find his or her walking speed solely based on smartphone sensors. However, existing smartphone-based approaches fail to provide acceptable precision for walking speed estimation. This leads to a question: is it possible to achieve comparable speed estimation accuracy using a smartphone over wearable sensor based obtrusive solutions? We find the answer from advanced neural networks. In this paper, we present DeepWalking, the first deep learning-based walking speed estimation scheme for smartphone. A deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of smartphone and to train the network model for accurate speed estimation. Experiments are performed with 10 participants using a treadmill. The average root-mean-squared-error (RMSE) of estimated walking speed is 0.16m/s which is comparable to the results obtained by state-of-the-art approaches based on a number of body-worn sensors (i.e., RMSE of 0.11m/s). The results indicate that a smartphone can be a strong tool for walking speed estimation if the sensor data are effectively calibrated and supported by advanced deep learning techniques.Comment: 6 pages, 9 figures, published in IEEE Global Communications Conference (GLOBECOM

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Synchronous wearable wireless body sensor network composed of autonomous textile nodes

A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system

Intelligent signal processing for digital healthcare monitoring

Ein gesunder Gang ist ein komplexer Prozess und erfordert ein Gleichgewicht zwischen verschiedenen neurophysiologischen Systemen im Körper und gilt als wesentlicher Indikator für den physischen und kognitiven Gesundheitszustand einer Person. Folglich würden Anwendungen im Bereich der Bioinformatik und des Gesundheitswesens erheblich von den Informationen profitieren, die sich aus einer längeren oder ständigen Überwachung des Gangs, der Gewohnheiten und des Verhaltens von Personen unter ihren natürlichen Lebensbedingungen und bei ihren täglichen Aktivitäten mit Hilfe intelligenter Geräte ergeben. Vergleicht man Trägheitsmess- und stationäre Sensorsysteme, so bieten erstere hervorragende Möglichkeiten für Ganganalyseanwendungen und bieten mehrere Vorteile wie geringe Größe, niedriger Preis, Mobilität und sind leicht in tragbare Systeme zu integrieren. Die zweiten gelten als der Goldstandard, sind aber teuer und für Messungen im Freien ungeeignet. Diese Arbeit konzentriert sich auf die Verbesserung der Zeit und Qualität der Gangrehabilitation nach einer Operation unter Verwendung von Inertialmessgeräten, indem sie eine neuartige Metrik zur objektiven Bewertung des Fortschritts der Gangrehabilitation in realen Umgebungen liefert und die Anzahl der verwendeten Sensoren für praktische, reale Szenarien reduziert. Daher wurden die experimentellen Messungen für eine solche Analyse in einer stark kontrollierten Umgebung durchgeführt, um die Datenqualität zu gewährleisten. In dieser Arbeit wird eine neue Gangmetrik vorgestellt, die den Rehabilitationsfortschritt anhand kinematischer Gangdaten von Aktivitäten in Innen- und Außenbereichen quantifiziert und verfolgt. In dieser Arbeit wird untersucht, wie Signalverarbeitung und maschinelles Lernen formuliert und genutzt werden können, um robuste Methoden zur Bewältigung von Herausforderungen im realen Leben zu entwickeln. Es wird gezeigt, dass der vorgeschlagene Ansatz personalisiert werden kann, um den Fortschritt der Gangrehabilitation zu verfolgen. Ein weiteres Thema dieser Arbeit ist die erfolgreiche Anwendung von Methoden des maschinellen Lernens auf die Ganganalyse aufgrund der großen Datenmenge, die von den tragbaren Sensorsystemen erzeugt wird. In dieser Arbeit wird das neuartige Konzept des ``digitalen Zwillings'' vorgestellt, das die Anzahl der verwendeten Wearable-Sensoren in einem System oder im Falle eines Sensorausfalls reduziert. Die Evaluierung der vorgeschlagenen Metrik mit gesunden Teilnehmern und Patienten unter Verwendung statistischer Signalverarbeitungs- und maschineller Lernmethoden hat gezeigt, dass die Einbeziehung der extrahierten Signalmerkmale in realen Szenarien robust ist, insbesondere für das Szenario mit Rehabilitations-Gehübungen in Innenräumen. Die Methodik wurde auch in einer klinischen Studie evaluiert und lieferte eine gute Leistung bei der Überwachung des Rehabilitationsfortschritts verschiedener Patienten. In dieser Arbeit wird ein Prototyp einer mobilen Anwendung zur objektiven Bewertung des Rehabilitationsfortschritts in realen Umgebungen vorgestellt

Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications