Verifying Safety Messages Using Relative-Time and Zone Priority in Vehicular Ad Hoc Networks.

In high-density road networks, with each vehicle broadcasting multiple messages per second, the arrival rate of safety messages can easily exceed the rate at which digital signatures can be verified. Since not all messages can be verified, algorithms for selecting which messages to verify are required to ensure that each vehicle receives appropriate awareness about neighbouring vehicles. This paper presents a novel scheme to select important safety messages for verification in vehicular ad hoc networks (VANETs). The proposed scheme uses location and direction of the sender, as well as proximity and relative-time between vehicles, to reduce the number of irrelevant messages verified (i.e., messages from vehicles that are unlikely to cause an accident). Compared with other existing schemes, the analysis results show that the proposed scheme can verify messages from nearby vehicles with lower inter-message delay and reduced packet loss and thus provides high level of awareness of the nearby vehicles.

Analysis of Optimal Sensor Positions for Activity Classification and Application on a Different Data Collection Scenario.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted, and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and a dataset collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values greater than 96%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

False alarm reduction in BSN-based cardiac monitoring using signal quality and activity type information.

False alarms in cardiac monitoring affect the quality of medical care, impacting on both patients and healthcare providers. In continuous cardiac monitoring using wireless Body Sensor Networks (BSNs), the quality of ECG signals can be deteriorated owing to several factors, e.g., noises, low battery power, and network transmission problems, often resulting in high false alarm rates. In addition, body movements occurring from activities of daily living (ADLs) can also create false alarms. This paper presents a two-phase framework for false arrhythmia alarm reduction in continuous cardiac monitoring, using signals from an ECG sensor and a 3D accelerometer. In the first phase, classification models constructed using machine learning algorithms are used for labeling input signals. ECG signals are labeled with heartbeat types and signal quality levels, while 3D acceleration signals are labeled with ADL types. In the second phase, a rule-based expert system is used for combining classification results in order to determine whether arrhythmia alarms should be accepted or suppressed. The proposed framework was validated on datasets acquired using BSNs and the MIT-BIH arrhythmia database. For the BSN dataset, acceleration and ECG signals were collected from 10 young and 10 elderly subjects while they were performing ADLs. The framework reduced the false alarm rate from 9.58% to 1.43% in our experimental study, showing that it can potentially assist physicians in diagnosing a vast amount of data acquired from wireless sensors and enhance the performance of continuous cardiac monitoring.

Automatic fall monitoring: a review.

Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address.

Toward continuous ambulatory monitoring using a wearable and wireless ECG- recording system: a study on the effects of signal quality on arrhythmia detection.

Five well-known arrhythmia classification algorithms were compared in this paper based on the recommendations in AAMI standard. They are C4.5, k-Nearest Neighbor, Multilayer Perceptron, PART, and Support Vector Machine, respectively, with inputs related to heartbeat intervals and ECG morphological features. They were evaluated on three independent datasets, including the MIT-BIH arrhythmia database, a collection of ECG signals acquired from healthy subjects by the wireless Body Sensor Network (BSN) nodes, and a third dataset captured also by the BSN nodes. Results showed the overall accuracy on the MIT-BIH arrhythmia database was approximately 99.04%, with high sensitivity, specificity, and selectivity. When tested with ECG signals acquired from the human subjects, which were partially deteriorated due to several factors, e.g., motion artifacts and data transmission problems, the overall accuracy of 94.19% and that of 81.22% were obtained for static activities and dynamic activities, respectively. In addition, the effects of the signal quality from these human subjects on false alarms were investigated. When false alarms occurring in signal segments with low quality were excluded, the number of false detections reduced from 14.17% to 8.65%. When evaluated on signals generated by the patient simulator, which included several types of premature ventricular contraction without artifacts from body movements, a high classification accuracy was also observed.

A method for shoulder range-of-motion estimation using a single wireless sensor node.

This study proposes a method for range-of-motion (ROM) estimation based on the acceleration and geomagnetic data acquired using a single miniaturized wireless sensor node. An experiment on eight shoulder rehabilitation protocols in real human subjects has been conducted, with a sensor placed on user's left and right upper arms and wrists. The experimental results demonstrate the limitations of estimation methods that use sensors placed on skin surface and that, despite being a different body segment, the wrist is a better placement position for sensor-based shoulder joint ROM measurement than the shoulder itself.

A haptic and auditory assistive user interface: helping the blinds on their computer operations.

In this paper, a study of assistive devices with multi-modal feedback is conducted to evaluate the efficiency of haptic and auditory information towards the users' mouse operations. Haptic feedback, generated by a combination of wheels driven by motors, is provided through the use of the haptic mouse. Meanwhile, audio feedback either in the form of synthesized directional speech or audio signal. Based on these interfaces, a set of experiments are conducted to compare their efficiencies. The measurement criteria used in this experiment are the distance regarding to the target circle in pixels, the operational time for the task in milliseconds, and opinion in term of understandability and comfortability towards each modal of the tested user interfaces in discrete indices. The experimental results show that with the proper modalities of feedback interfaces for the user, the efficiency can be improved by either the reduction in operational time or the increase of accuracy in pointing the target. Furthermore, the justification is also based on the user's satisfaction towards using the device to conduct the predefined cursor movement task, which occasionally is difficult to understand and interpret by the user. For example of the application adopting the proposed interface system, a web browser application is implemented and explained in this paper.

Optimal feature selection applied to multispectral fluorescence imaging.

Recent rapid developments in multi-modal optical imaging have created a significant clinical demand for its in vivo--in situ application. This offers the potential for real-time tissue characterization, functional assessment, and intra-operative guidance. One of the key requirements for in vivo consideration is to minimise the acquisition window to avoid tissue motion and deformation, whilst making the best use of the available photons to account for correlation or redundancy between different dimensions. The purpose of this paper is to propose a feature selection framework to identify the best combination of features for discriminating between different tissue classes such that redundant or irrelevant information can be avoided during data acquisition. The method is based on a Bayesian framework for feature selection by using the receiver operating characteristic curves to determine the most pertinent data to capture. This represents a general technique that can be applied to different multi-modal imaging modalities and initial results derived from phantom and ex vivo tissue experiments demonstrate the potential clinical value of the technique.

A probabilistic framework for tracking deformable soft tissue in minimally invasive surgery.

The use of vision based algorithms in minimally invasive surgery has attracted significant attention in recent years due to its potential in providing in situ 3D tissue deformation recovery for intra-operative surgical guidance and robotic navigation. Thus far, a large number of feature descriptors have been proposed in computer vision but direct application of these techniques to minimally invasive surgery has shown significant problems due to free-form tissue deformation and varying visual appearances of surgical scenes. This paper evaluates the current state-of-the-art feature descriptors in computer vision and outlines their respective performance issues when used for deformation tracking. A novel probabilistic framework for selecting the most discriminative descriptors is presented and a Bayesian fusion method is used to boost the accuracy and temporal persistency of soft-tissue deformation tracking. The performance of the proposed method is evaluated with both simulated data with known ground truth, as well as in vivo video sequences recorded from robotic assisted MIS procedures.