21 research outputs found
LAMP assay and rapid sample preparation method for on-site detection of flavescence dorée phytoplasma in grapevine
Cysteine proteinase inhibitor cystatin C in squamous cell carcinoma of the head and neck: relation to prognosis
Gesture recognition of the upper limbs - From signal to symbol
Fröhlich M, Wachsmuth I. Gesture recognition of the upper limbs - From signal to symbol. In: Wachsmuth I, Fröhlich M, eds. Gesture and Sign Language in Human-Computer Interaction. LNAI 1371. Berlin: Springer; 1998: 173-184.To recognise gestures performed by people without disabilities during verbal communication – so-called coverbal gestures – a flexible system with task-oriented design is proposed. The issue of flexibility is addressed via different kinds of – grasped as agents –, which are grouped in different levels. They can be easily reconfigured or rewritten to suit another application. This system of layered agents uses an abstract body-model to transform the up-taken data from the six-degree-of-freedom-sensors, and the data gloves, to a first-level symbolic description of gesture features. In a first integration step the first-level symbols are integrated to second-level symbols describing a whole gesture. Second-level symbolic gesture descriptions are the entities which can be integrated with speech tokens to form multi-modal utterances
Simultaneous Detection of RNA and DNA Targets Based on Multiplex Isothermal Amplification
Probing the Activity Modification Space of the Cysteine Peptidase Cathepsin K with Novel Allosteric Modifiers
Loop-Mediated Isothermal Amplification of Specific Endoglucanase Gene Sequence for Detection of the Bacterial Wilt Pathogen Ralstonia solanacearum
Detection of root-infecting fungi on cool-season turfgrasses using loop-mediated isothermal amplification and recombinase polymerase amplification
Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices
Abstract Background End-effector robots are commonly used in robot-assisted neuro-rehabilitation therapies for upper limbs where the patient’s hand can be easily attached to a splint. Nevertheless, they are not able to estimate and control the kinematic configuration of the upper limb during the therapy. However, the Range of Motion (ROM) together with the clinical assessment scales offers a comprehensive assessment to the therapist. Our aim is to present a robust and stable kinematic reconstruction algorithm to accurately measure the upper limb joints using only an accelerometer placed onto the upper arm. Methods The proposed algorithm is based on the inverse of the augmented Jaciobian as the algorithm (Papaleo, et al., Med Biol Eng Comput 53(9):815–28, 2015). However, the estimation of the elbow joint location is performed through the computation of the rotation measured by the accelerometer during the arm movement, making the algorithm more robust against shoulder movements. Furthermore, we present a method to compute the initial configuration of the upper limb necessary to start the integration method, a protocol to manually measure the upper arm and forearm lengths, and a shoulder position estimation. An optoelectronic system was used to test the accuracy of the proposed algorithm whilst healthy subjects were performing upper limb movements holding the end effector of the seven Degrees of Freedom (DoF) robot. In addition, the previous and the proposed algorithms were studied during a neuro-rehabilitation therapy assisted by the ‘PUPArm’ planar robot with three post-stroke patients. Results The proposed algorithm reports a Root Mean Square Error (RMSE) of 2.13cm in the elbow joint location and 1.89cm in the wrist joint location with high correlation. These errors lead to a RMSE about 3.5 degrees (mean of the seven joints) with high correlation in all the joints with respect to the real upper limb acquired through the optoelectronic system. Then, the estimation of the upper limb joints through both algorithms reveal an instability on the previous when shoulder movement appear due to the inevitable trunk compensation in post-stroke patients. Conclusions The proposed algorithm is able to accurately estimate the human upper limb joints during a neuro-rehabilitation therapy assisted by end-effector robots. In addition, the implemented protocol can be followed in a clinical environment without optoelectronic systems using only one accelerometer attached in the upper arm. Thus, the ROM can be perfectly determined and could become an objective assessment parameter for a comprehensive assessment