Map++: A Crowd-sensing System for Automatic Map Semantics Identification

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2014

Detection of static and dynamic activities using uniaxial accelerometers

Rehabilitation treatment may be improved by objective analysis of activities of daily living. For this reason, the feasibility of distinguishing several static and dynamic activities (standing, sitting, lying, walking, ascending stairs, descending stairs, cycling) using a small set of two or three uniaxial accelerometers mounted on the body was investigated. The accelerometer signals can be measured with a portable data acquisition system, which potentially makes it possible to perform online detection of static and dynamic activities in the home environment. However, the procedures described in this paper have yet to be evaluated in the home environment. Experiments were conducted on ten healthy subjects, with accelerometers mounted on several positions and orientations on the body, performing static and dynamic activities according to a fixed protocol. Specifically, accelerometers on the sternum and thigh were evaluated. These accelerometers were oriented in the sagittal plane, perpendicular to the long axis of the segment (tangential), or along this axis (radial). First, discrimination between the static or dynamic character of activities was investigated. This appeared to be feasible using an rms-detector applied on the signal of one sensor tangentially mounted on the thigh. Second, the distinction between static activities was investigated. Standing, sitting, lying supine, on a side and prone could be distinguished by observing the static signals of two accelerometers, one mounted tangentially on the thigh, and the second mounted radially on the sternum. Third, the distinction between the cyclical dynamic activities walking, stair ascent, stair descent and cycling was investigated. The discriminating potentials of several features of the accelerometer signals were assessed: the mean value, the standard deviation, the cycle time and the morphology. Signal morphology was expressed by the maximal cross-correlation coefficients with template signals for the different dynamic activities. The mean signal values and signal morphology of accelerometers mounted tangentially on the thigh and the sternum appeared to contribute to the discrimination of dynamic activities with varying detection performances. The standard deviation of the signal and the cycle time were primarily related to the speed of the dynamic activities, and did not contribute to the discrimination of the activities. Therefore, discrimination of dynamic activities on the basis of the combined evaluation of the mean signal value and signal morphology is propose

Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices

Design of a terrain detection system for foot drop

The ankle foot orthotic (AFO) has been around for centuries. They were created to augment functionality of an ankle damaged due to injury or disease. A common reason a patient might be prescribed an AFO is a condition called foot drop. Foot drop can be caused by many conditions, but the most common reason is a stroke. Foot drop can be characterized by the inability to raise and/or lower a patient\u27s foot. This incapacitation of the patient\u27s foot leads to unnatural gaits and joint fatigue, as well as increasing the patient\u27s likelihood of tripping and becoming seriously injured. Hard plastic AFOs that hold a patient\u27s foot in a neutral position are the current standard for combating foot drop. These AFOs come in many different shapes and sizes, which emphasizes the wide variety in functionality of someone with foot drop. Unfortunately, the restrictive nature of the AFO can cause unnatural movements in the patient\u27s foot; these unnatural tendencies are more exaggerated when walking down stairs and ramps, as the natural gait is to land toe first, the opposite of what the brace allows the patient to do. The purpose of this project is to create a sensor system for an AFO to help identify varying terrain. In the future this information can then be made to control an active AFO. Each terrain type will be first measured by a pair of simple infrared range finder, attached on the lower leg, one range finder looks ahead of the user and the other looks straight down at the ground. Models for the ground conditions can be established by representing each with Fourier series created using RANdom Sample Consensus (RANSAC). RANSAC coefficients will be scaled off the rate of data coming in and gait speed. Each model has a period term so the data can easily be scaled to match the pattern of walking regardless of pace. Gait speed will be measured using the downward facing ankle-mounted rangefinder, but with a threshold to determine when the foot is in contact with the ground. Once this initial set-up is completed, the system can take in data live and provide a prediction of the type of ground the patient is walking over, using pattern recognition techniques. The hope for this project is that if the system can accurately predict the change in ground type from, for example, level walking to walking down a ramp, an AFO could then be made to adjust itself, giving the patient a more natural gait, even when encountering adverse conditions. A byproduct of constantly using a patient\u27s own gait to measure ground type is the ability to track a patient\u27s changing gait over time, giving therapists a valuable new tool for tracking progress in a patient

Multi-sensor fusion based on multiple classifier systems for human activity identification

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system. - 2019, The Author(s).This research is supported by University of Malaya BKP Special Grant no vote BKS006-2018.Scopu

Stair Gait Classification from Kinematic Sensors

Gait measurement is of interest for both orthopedists and biomechanical engineers. It is useful for analysis of gait disorders and in design of orthotic and prosthetic devices. In this chapter an algorithm is presented to suit estimation of one foot angle in the sagital plane, independent on gait conditions. Only one gyro is used during swing and two accelerometers are needed for calibration during stance. Also, the sensor placement at the front foot avoids the need for heel strike for stance transition. Stair walking can therefore be studied. From the estimated swing trajectory three different gait conditions: up stair, horizontal and down stair are classified.Abstracting and non-profit use of the material is permitted with the credit to the source. After this work has been published by the I-Tech Education and Publishing, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. (www.ars-journal.com)</p