The Leap Motion Controller (LMC) is a hand-held, non-professional motion capture device that is able to evaluate different motor tasks in a clinical setting. In this study, its accuracy was verified, evaluating its performance in the acquisition of movements based on the simulation of the Archimedean spiral test. We present an innovative method for obtaining information on fine motor skills during the spiral drawing test, using an instrumented evaluation platform based on the LMC. First, several experimental conditions were carried out that challenged the performance of the LMC during the simulation of the spiral test, then a case study was conducted to establish the validity of its use to evaluate the spiral drawing test in humans. Due to this research it is possible to extrapolate the spiral drawing test from a two-dimensional space to a three-dimensional space, without restrictions, or contact with objects and in a controlled clinical environment. Allowing to obtain variables of the kinematics of the hand during a spiral drawing task in a three-dimensional space in future.
The muscle coactivation phenomenon (mCoA) has been used to describe motor tasks such as human gait, reflecting the levels of joint stability of the lower limbs in healthy people versus patients with musculoskeletal problems. Nonetheless, scarce literature has described the mCoA evolution in patients with Achilles tendon reconstruction surgery. The aim of this study was to describe the mCoA during human gait in one patient undergoing Achilles tendon reconstruction surgery for acute tendon rupture with a new surgical technique (PARS-Dresden) in two times-frame (12 and 24 weeks). Human gait was recorded on a treadmill at 2.5 km/h. This task was recorded with a 3D motion system synchronized with electromyographic (sEMG) equipment. The sEMG signals were acquired from three portions of the triceps surae (soleus [So], medial [MG], and lateral [LG] gastrocnemius) and tibialis anterior [TA], in both shanks. The mCoA levels (represented as a percentage) were calculated considering twenty gait cycles in muscular pairs TA-MG, TA-LG, and TA-So, in both operated (OL) and non-operated (NOL) limbs. The analysis was estimated with a processing pipeline developed in Python language, considering the Falcone and Winter equation. The results showed a slight increase in the mCoA during gait in some muscular pairs at 24 weeks. The coactivation levels were greater in the TA-MG and TA-So pairs of the OL vs the NOL. Additionally, a higher asymmetry was observed between OL vs NOL at the 12 weeks.
Graph Signal Processing (GSP) is a framework for analyzing signals defined over a graph. Considering the electrodes used to record the electroencephalogram (EEG) as a sensor network makes it possible to use GSP to analyze EEG signals. Using the graph over which the signal is defined allows one to take advantage of a signal structure that is ignored by classic signal processing approaches. However, there are many details about how to use GSP to analyze the EEG that are not studied in the literature. Here we show an example of how to impute missing EEG data using GSP. We show that GSP allows reconstructing EEG missing data with a lower error than a classic approach based on radial basis functions, confirming that the underlying graph over a graph over which the signal is defined contains relevant information that can be exploited to improve a given signal processing task. By studying two approaches for building the graph (k-nearest neighbors and a thresholded Gaussian kernel) and the effect of its parameter, we highlight the importance of building the graph appropriately. These results show the potential of incorporating GPS techniques into the EEG processing pipeline.
KEYWORDS: Sensors, Mining, Received signal strength, Signal to noise ratio, Relays, Robotics, Control systems, Land mines, RF communications, Radio propagation
Tunnels are a challenging environment for radio communications. In this paper we consider the use of autonomous
mobile radio nodes (AMRs) to provide wireless tethering between a base station and a leader in a tunnel exploration
scenario. Using a realistic, experimentally-derived underground radio signal propagation model and a tethering
algorithm for AMR motion control based on a consensus variable protocol, we present experimental results involving a
tele-operated leader with one or two followers. Using radio signal strength measurements, the followers autonomously
space themselves so as to achieve equal radio distance between each entity in the chain from the base to the leader.
Results show the feasibility of our ideas.
Much of the success of small unmanned air vehicles (UAVs) has arguably been due to the widespread availability of
low-cost, portable autopilots. While the development of unmanned ground vehicles (UGVs) has led to significant
achievements, as typified by recent grand challenge events, to date the UGV equivalent of the UAV autopilot
is not available. In this paper we describe our recent research aimed at the development of a generic UGV
autopilot. Assuming we are given a drive-by-wire vehicle that accepts as inputs steering, brake, and throttle
commands, we present a system that adds sonar ranging sensors, GPS/IMU/odometry, stereo camera, and
scanning laser sensors, together with a variety of interfacing and communication hardware. The system also
includes a finite state machine-based software architecture as well as a graphical user interface for the operator
control unit (OCU). Algorithms are presented that enable an end-to-end scenario whereby an operator can view
stereo images as seen by the vehicle and can input GPS waypoints either from a map or in the vehicle's scene-view
image, at which point the system uses the environmental sensors as inputs to a Kalman filter for pose estimation
and then computes control actions to move through the waypoint list, while avoiding obstacles. The long-term
goal of the research is a system that is generically applicable to any drive-by-wire unmanned ground vehicle.
Conference Committee Involvement (1)
Tenth International Symposium on Medical Information Processing and Analysis
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.