Quantifying Passive Biomechanical Stability Using an Industrial Robot: Development and Experimental Validation of a Task Space Motion Framework

This paper presents a methodology and generalized motion framework for quantifying passive biomechanical stability and Range of Motion of human cadaveric specimens, using a position-controlled industrial robot and a wrist-mounted force/torque sensor. Many biomechanical studies on diarthrodial joints using human cadaveric specimens are published in the literature, using various test protocols and machines to apply the loading conditions. In these studies, laxity or mobility of the joints are quantified by measuring the magnitude of translations and rotations with respect to force and torque. The protocols and anatomical motions of the specimens are usually described high-level, textually, and from a medical perspective to a broad audience. The present paper aims to describe, from a technical perspective to a robotics audience, our method to perform biomechanical studies and how existing protocols can be replicated through parameterization using the existing textual descriptions. To accomplish this, we propose a generalized task space motion framework for performing biomechanical studies on diarthrodial joints. The generalization is made by defining the robot Tool Center Point at the cadaveric joint rotation center and aligning the specimen so the anatomical motions can be modeled in world frame or tool frame. The framework was successfully evaluated in a technical pilot study on the shoulder, using one cadaveric shoulder specimen and an established protocol from the literature. The specimen was tested in the intact state and in an injury state, with increased passive instability observed for the injury state compared to intact state.

View this article on IEEE Xplore

An In-Depth Study on Open-Set Camera Model Identification

Camera model identification refers to the problem of linking a picture to the camera model used to shoot it. In this paper, as this might be an enabling factor in different forensic applications to single out possible suspects (e.g., detecting the author of child abuse or terrorist propaganda material), many accurate camera model attribution methods have been developed. One of their main drawbacks, however, are a typical closed-set assumption of the problem. This means that an investigated photograph is always assigned to one camera model within a set of known ones present during the investigation, i.e., training time. The fact that a picture can come from a completely unrelated camera model during actual testing is usually ignored. Under realistic conditions, it is not possible to assume that every picture under analysis belongs to one of the available camera models. In this paper, to deal with this issue, we present an in-depth study on the possibility of solving the camera model identification problem in open-set scenarios. Given a photograph, we aim at detecting whether it comes from one of the known camera models of interest or from an unknown one. We compare different feature extraction algorithms and classifiers especially targeting open-set recognition. We also evaluate possible open-set training protocols that can be applied along with any open-set classifier, observing that a simple alternative among the selected ones obtains the best results. Thorough testing on independent datasets show that it is possible to leverage a recently proposed convolutional neural network as feature extractor paired with a properly trained open-set classifier aiming at solving the open-set camera model attribution problem even on small-scale image patches, improving over the state-of-the-art available solutions.

View this article on IEEE Xplore

Most Cited Article of 2017: Lightweight three-factor authentication and key agreement protocol for internet-integrated wireless sensor networks

Wireless sensor networks (WSNs) will be integrated into the future Internet as one of the components of the Internet of Things, and will become globally addressable by any entity connected to the Internet. Despite the great potential of this integration, it also brings new threats, such as the exposure of sensor nodes to attacks originating from the Internet. In this context, lightweight authentication and key agreement protocols must be in place to enable end-to-end secure communication. Recently, Amin et al. proposed a three-factor mutual authentication protocol for WSNs. However, we identified several flaws in their protocol. We found that their protocol suffers from smart card loss attack where the user identity and password can be guessed using offline brute force techniques. Moreover, the protocol suffers from known session-specific temporary information attack, which leads to the disclosure of session keys in other sessions. Furthermore, the protocol is vulnerable to tracking attack and fails to fulfill user untraceability. To address these deficiencies, we present a lightweight and secure user authentication protocol based on the Rabin cryptosystem, which has the characteristic of computational asymmetry. We conduct a formal verification of our proposed protocol using ProVerif in order to demonstrate that our scheme fulfills the required security properties. We also present a comprehensive heuristic security analysis to show that our protocol is secure against all the possible attacks and provides the desired security features. The results we obtained show that our new protocol is a secure and lightweight solution for authentication and key agreement for Internet-integrated WSNs.

View this article on IEEE Xplore