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

A Study on Human Fall Detection Systems: Daily Activity Classification and Sensing Techniques

Fall detection for elderly is a major topic as far as assistive technologies are concerned. This is due to the high demand for the products and technologies related to fall detection with the ageing population around the globe. This paper gives a review of previous works on human fall detection devices and a preliminary results from a developing depth sensor based device. The three main approaches used in fall detection devices such as wearable based devices, ambient based devices and vision based devices are identified along with the sensors employed.  The frameworks and algorithms applied in each of the approaches and their uniqueness is also illustrated. After studying the performance and the shortcoming of the available systems a future solution using depth sensor is also proposed with preliminary results

Can smartwatches replace smartphones for posture tracking?

This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed

An enhanced sensor-based approach for evaluation of a geriatric fall risk in non-ambulatory environments

Jedes Jahr stürzt rund ein Drittel der über 65 Jährigen. Stürze sind die Hauptursache für mittlere bis schwere Verletzungen und damit eine enorme Belastung für das Gesundheitssystem. Eine zeitlich akkurate Sturzrisikobewertung in einer breit akzeptierten und nicht-stigmatisierenden Art und Weise kann zu signifikanten Veränderungen in der Strategie der Sturzprävention führen und damit dazu beitragen, die Anzahl der stürzenden Personen, sowie die Sturzrate zu reduzieren. Die gegenwärtige klinische Evaluierung des Sturzrisikos ist zeitaufwendig und subjektiv. Folglich sind Bewertungen in stationärem Umfeld obstruktiv, oder fokussieren sich ausschließlich auf einmalige, periodische Merkmale der menschlichen Bewegung. Der Fokus dieser Arbeit liegt in der Erforschung und Definition neuer Konzepte zur Beurteilung der Koordination der Extremitäten, der Art des Gehens und der Aufstehvorgänge anhand von Signalen von am Handgelenk getragener Inertial- und Umgebungssensorik. Merkmale im Zeit- und Frequenzraum wurden händisch entwickelt, um daraus Support Vector Maschine -Modelle abzuleiten. Die Modelle beschreiben die physikalische Leistungsfähigkeit einer Person in Form einer objektiven (quantitativen) Sturzrisikobewertung in einem störungsanfälligen häuslichen Umfeld. Für erste Untersuchungszwecke wurde eine Forschungsstudie mit 28 älteren Teilnehmern in einem kontrollierten Umfeld durchgeführt. Darauf aufsetzend wurde eine große Querschnittsstudie mit einer Kohorte von 180 Probanden durchgeführt. Eine sich der Messwoche anschließende sechsmonatige Nachverfolgungsphase wurde zur Validierung der Modelle in die Studie inkludiert. Die Ergebnisse haben einen neuen Prädiktor für akutes Sturzrisiko hervorgebracht. Zusätzlich konnte aufgezeigt werden, dass die Kenntnis der Umgebungsbedingungen relevant sind, um die menschlichen Bewegungen richtig bewerten zu können. Ein innovativer Echtzeitalgorithmus wurde entwickelt, in dem Multi-Sensor-Ansätze fusioniert, sowie auf Bewegung basierende Filter integriert sind. Die Einflüsse der Hand-Abhängigkeit auf die Leistungsfähigkeit des Algorithmus konnten im Rahmen dieser Arbeit untersucht werden. Die Validierung der entwickelten Modelle in allen drei Domänen gegen die Grundwahrheit zeigt eine klinisch relevante Genauigkeit oder zumindest teilweise bessere Ergebnisse gegenüber dem Stand der Technik. Die Studie zeigt die Möglichkeit auf, Einschränkungen klinischer Tests zu bewältigen, sowie in Armbändern integrierte Sensorik sowohl für eine akute, wie auch eine konventionelle Sechsmontasbewertung des Sturzrisikos verlässlich anzuwenden

Reliable and secure body fall detection algorithm in a wireless mesh network

Falls in elderly is one of the most serious causes of severe injury. Lack in immediate medical help makes these injuries life threatening. An automatic fall detection system, presented in this research, would help reduce the arrival time of medical attention, reduce mortality rate and promote independent living. Therefore, the algorithm finds its application in the medical field, specifically in nursing homes. The system designed and presented in this research is not only capable of detecting human falls but also distinguishing them from routine fall-like activities. Falls are detected with the help of a small wearable embedded device, i.e. Texas Instruments\u27 eZ430 Chronos watch which is wireless development kit. The watch operates at an RF frequency of 915MHz to communicate with each other in a wireless network. The wearable wrist watch is programmable and has an in-built accelerometer sensor and microcontroller circuitry. The accelerometer sensor is motion sensitive and measures the acceleration due to gravity. Whenever a fall is detected the watch sends a signal to the neighboring watch, which is always in the monitoring mode. Signal transmission and reception between these devices is via wireless communication, where every node is a sensor forwarding the signal to the next node. A wireless mesh network helps in quick transmission of signals thereby alerting the authorities. In order to differentiate between body fall and Activities of Daily Life, various body motions and gestures have been studied and presented. The features of a real fall and that of normal human motions are extracted and analyzed from the data obtained by volunteers who participated in the research. Evaluation of results led to setting forth threshold values for parameters like acceleration, change in co-ordinate axes and angle of orientation. Over-passing the threshold raises a fall alarm to bring to the attention of the hospital authority

Computer Vision Algorithms for Mobile Camera Applications

Wearable and mobile sensors have found widespread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, including accelerometer, gyroscope, magnetometer, microphone and camera, it has become more feasible to develop algorithms for activity monitoring, guidance and navigation of unmanned vehicles, autonomous driving and driver assistance, by using data from one or more of these sensors. In this thesis, we focus on multiple mobile camera applications, and present lightweight algorithms suitable for embedded mobile platforms. The mobile camera scenarios presented in the thesis are: (i) activity detection and step counting from wearable cameras, (ii) door detection for indoor navigation of unmanned vehicles, and (iii) traffic sign detection from vehicle-mounted cameras. First, we present a fall detection and activity classification system developed for embedded smart camera platform CITRIC. In our system, the camera platform is worn by the subject, as opposed to static sensors installed at fixed locations in certain rooms, and, therefore, monitoring is not limited to confined areas, and extends to wherever the subject may travel including indoors and outdoors. Next, we present a real-time smart phone-based fall detection system, wherein we implement camera and accelerometer based fall-detection on Samsung Galaxy S™ 4. We fuse these two sensor modalities to have a more robust fall detection system. Then, we introduce a fall detection algorithm with autonomous thresholding using relative-entropy within the class of Ali-Silvey distance measures. As another wearable camera application, we present a footstep counting algorithm using a smart phone camera. This algorithm provides more accurate step-count compared to using only accelerometer data in smart phones and smart watches at various body locations. As a second mobile camera scenario, we study autonomous indoor navigation of unmanned vehicles. A novel approach is proposed to autonomously detect and verify doorway openings by using the Google Project Tango™ platform. The third mobile camera scenario involves vehicle-mounted cameras. More specifically, we focus on traffic sign detection from lower-resolution and noisy videos captured from vehicle-mounted cameras. We present a new method for accurate traffic sign detection, incorporating Aggregate Channel Features and Chain Code Histograms, with the goal of providing much faster training and testing, and comparable or better performance, with respect to deep neural network approaches, without requiring specialized processors. Proposed computer vision algorithms provide promising results for various useful applications despite the limited energy and processing capabilities of mobile devices

Simple Fall Criteria for MEMS Sensors: Data Analysis and Sensor Concept

This paper presents a new and simple fall detection concept based on detailed experimental data of human falling and the activities of daily living (ADLs). Establishing appropriate fall algorithms compatible with MEMS sensors requires detailed data on falls and ADLs that indicate clearly the variations of the kinematics at the possible sensor node location on the human body, such as hip, head, and chest. Currently, there is a lack of data on the exact direction and magnitude of each acceleration component associated with these node locations. This is crucial for MEMS structures, which have inertia elements very close to the substrate and are capacitively biased, and hence, are very sensitive to the direction of motion whether it is toward or away from the substrate. This work presents detailed data of the acceleration components on various locations on the human body during various kinds of falls and ADLs. A two-degree-of-freedom model is used to help interpret the experimental data. An algorithm for fall detection based on MEMS switches is then established. A new sensing concept based on the algorithm is proposed. The concept is based on employing several inertia sensors, which are triggered simultaneously, as electrical switches connected in series, upon receiving a true fall signal. In the case of everyday life activities, some or no switches will be triggered resulting in an open circuit configuration, thereby preventing false positive. Lumped-parameter model is presented for the device and preliminary simulation results are presented illustrating the new device concept