Multi sensor system for pedestrian tracking and activity recognition in indoor environments

The widespread use of mobile devices and the rise of Global Navigation Satellite Systems (GNSS) have allowed mobile tracking applications to become very popular and valuable in outdoor environments. However, tracking pedestrians in indoor environments with Global Positioning System (GPS)-based schemes is still very challenging. Along with indoor tracking, the ability to recognize pedestrian behavior and activities can lead to considerable growth in location-based applications including pervasive healthcare, leisure and guide services (such as, hospitals, museums, airports, etc.), and emergency services, among the most important ones. This paper presents a system for pedestrian tracking and activity recognition in indoor environments using exclusively common off-the-shelf sensors embedded in smartphones (accelerometer, gyroscope, magnetometer and barometer). The proposed system combines the knowledge found in biomechanical patterns of the human body while accomplishing basic activities, such as walking or climbing stairs up and down, along with identifiable signatures that certain indoor locations (such as turns or elevators) introduce on sensing data. The system was implemented and tested on Android-based mobile phones. The system detects and counts steps with an accuracy of 97% and 96:67% in flat floor and stairs, respectively; detects user changes of direction and altitude with 98:88% and 96:66% accuracy, respectively; and recognizes the proposed human activities with a 95% accuracy. All modules combined lead to a total tracking accuracy of 91:06% in common human motion indoor displacement

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

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Studies on Sensor Aided Positioning and Context Awareness

This thesis studies Global Navigation Satellite Systems (GNSS) in combination with sensor systems that can be used for positioning and obtaining richer context information. When a GNSS is integrated with sensors, such as accelerometers, gyroscopes and barometric altimeters, valuable information can be produced for several applications; for example availability or/and performance of the navigation system can be increased. In addition to position technologies, GNSS devices are integrated more often with different types of technologies to fulfil several needs, e.g., different types of context recognition. The most common integrated devices are accelerometer, gyroscope, and magnetometer but also other sensors could be used.More specifically, this thesis presents sensor aided positioning with two satellite signals with altitude assistance. The method uses both pseudorange and Doppler measurements. The system is required to be stationary during the process and a source of altitude information, e.g., a MEMS barometer, is needed in addition to a basic GNSS receiver. Authentic pseudorange and Doppler measurements with simulated altitude were used used to test the algorithm. Results showed that normally the accuracy of couple of kilometers is acquired. Thesis also studies on what kind of errors barometric altimeter might encounter especially in personal positioning. The results show that barometers in differential mode provide highly accurate altitude solution (within tens of centimeters), but local disturbances in pressure need to be acknowledged in the application design. For example, heating, ventilating, and air conditioning in a car can have effect of few meters. Thus this could cause problems if the barometer is used as a altimeter for under meter-level positioning or navigation.We also explore methods for sensor aided GNSS systems for context recognition. First, the activity and environment recognition from mobile phone sensor and radio receiver data is investigated. The aim is in activity (e.g., walking, running, or driving a vehicle) and environment (e.g., street, home, or restaurant) detection. The thesis introduces an algorithm for user specific adaptation of the context model parameters using the feedback from the user, which can provide a confidence measure about the correctness of a classification. A real-life data collection campaign validate the proposed method. In addition, the thesis presents a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to the motorist’s helmet, torso of the motorist, and to the rear of the motor cycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. Crash dummy tests were done by throwing the dummy from different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. Preliminary results proved the potential of the proposed method could be applicable in real situations. In all the proposed systems in this thesis, knowledge of the context can help the positioning system, but also positioning system can help in determining the context

Design and Effect of Continuous Wearable Tactile Displays

Our sense of touch is one of our core senses and while not as information rich as sight and hearing, it tethers us to reality. Our skin is the largest sensory organ in our body and we rely on it so much that we don\u27t think about it most of the time. Tactile displays - with the exception of actuators for notifications on smartphones and smartwatches - are currently understudied and underused. Currently tactile cues are mostly used in smartphones and smartwatches to notify the user of an incoming call or text message. Specifically continuous displays - displays that do not just send one notification but stay active for an extended period of time and continuously communicate information - are rarely studied. This thesis aims at exploring the utilization of our vibration perception to create continuous tactile displays. Transmitting a continuous stream of tactile information to a user in a wearable format can help elevate tactile displays from being mostly used for notifications to becoming more like additional senses enabling us to perceive our environment in new ways. This work provides a serious step forward in design, effect and use of continuous tactile displays and their use in human-computer interaction. The main contributions include: Exploration of Continuous Wearable Tactile Interfaces This thesis explores continuous tactile displays in different contexts and with different types of tactile information systems. The use-cases were explored in various domains for tactile displays - Sports, Gaming and Business applications. The different types of continuous tactile displays feature one- or multidimensional tactile patterns, temporal patterns and discrete tactile patterns. Automatic Generation of Personalized Vibration Patterns In this thesis a novel approach of designing vibrotactile patterns without expert knowledge by leveraging evolutionary algorithms to create personalized vibration patterns - is described. This thesis presents the design of an evolutionary algorithm with a human centered design generating abstract vibration patterns. The evolutionary algorithm was tested in a user study which offered evidence that interactive generation of abstract vibration patterns is possible and generates diverse sets of vibration patterns that can be recognized with high accuracy. Passive Haptic Learning for Vibration Patterns Previous studies in passive haptic learning have shown surprisingly strong results for learning Morse Code. If these findings could be confirmed and generalized, it would mean that learning a new tactile alphabet could be made easier and learned in passing. Therefore this claim was investigated in this thesis and needed to be corrected and contextualized. A user study was conducted to study the effects of the interaction design and distraction tasks on the capability to learn stimulus-stimulus-associations with Passive Haptic Learning. This thesis presents evidence that Passive Haptic Learning of vibration patterns induces only a marginal learning effect and is not a feasible and efficient way to learn vibration patterns that include more than two vibrations. Influence of Reference Frames for Spatial Tactile Stimuli Designing wearable tactile stimuli that contain spatial information can be a challenge due to the natural body movement of the wearer. An important consideration therefore is what reference frame to use for spatial cues. This thesis investigated allocentric versus egocentric reference frames on the wrist and compared them for induced cognitive load, reaction time and accuracy in a user study. This thesis presents evidence that using an allocentric reference frame drastically lowers cognitive load and slightly lowers reaction time while keeping the same accuracy as an egocentric reference frame, making a strong case for the utilization of allocentric reference frames in tactile bracelets with several tactile actuators

A survey on wearable sensor modality centred human activity recognition in health care

Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR