RNN-based approaches have achieved outstanding performance on action recognition with skeleton inputs. Currently these methods limit their inputs to coordinates of joints and improve the accuracy mainly by extending RNN models to spatial domains in various ways. While such models explore relations between different parts directly from joint coordinates, we provide a simple universal spatial modeling method perpendicular to the RNN model enhancement. Specifically, we select a set of simple geometric features, motivated by the evolution of previous work. With experiments on a 3-layer LSTM framework, we observe that the geometric relational features based on distances between joints and selected lines outperform other features and achieve state-of-art results on four datasets. Further, we show the sparsity of input gate weights in the first LSTM layer trained by geometric features and demonstrate that utilizing joint-line distances as input require less data for training.

On Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks

Recent skeleton-based action recognition approaches achieve great improvement by using recurrent neural network (RNN) models. Currently, these approaches build an end-to-end network from coordinates of joints to class categories and improve accuracy by extending RNN to spatial domains. First, while such well-designed models and optimization strategies explore relations between different parts directly from joint coordinates, we provide a simple universal spatial modeling method perpendicular to the RNN model enhancement. Specifically, according to the evolution of previous work, we select a set of simple geometric features, and then seperately feed each type of features to a three-layer LSTM framework. Second, we propose a multistream LSTM architecture with a new smoothed score fusion technique to learn classification from different geometric feature streams. Furthermore, we observe that the geometric relational features based on distances between joints and selected lines outperform other features and the fusion results achieve the state-of-the-art performance on four datasets. We also show the sparsity of input gate weights in the first LSTM layer trained by geometric features and demonstrate that utilizing joint-line distances as input require less data for training.

Fusing Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks

In view of increasing collections of available 3D motion capture (mocap) data, the task of automatically annotating large sets of unstructured motion data is gaining in importance. In this paper, we present an efficient approach to label mocap data according to a given set of motion categories or classes, each specified by a suitable set of positive example motions. For each class, we derive a motion template that captures the consistent and variable aspects of a motion class in an explicit matrix representation. We then present a novel annotation procedure, where the unknown motion data is segmented and annotated by locally comparing it with the available motion templates. This procedure is supported by an efficient keyframe-based preprocessing step, which also significantly improves the annotation quality by eliminating false positive matches. As a further contribution, we introduce a genetic learning algorithm to automatically learn the necessary keyframes from the given example motions. For evaluation, we report on various experiments conducted on two freely available sets of motion capture data (CMU and HDM05).

Efficient and Robust Annotation of Motion Capture Data

As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. In this article, a novel impedance modulation strategy of a lower-limb exoskeleton is proposed to provide appropriate power and balance assistance during STS movements while preserving the wearer’s control priority. The impedance modulation control strategy ensures adaptation of the mechanical impedance of the human–exoskeleton system toward a desired one requiring less wearer’s effect while reinforcing the wearer’s balance control ability during STS movements. A human joint torque observer is designed to estimate the joint torques developed by the wearer using joint position kinematics instead of electromyography or force sensors; a time-varying desired impedance model is proposed according to the wearer’s lower-limb motion ability. A virtual environmental force is designed for balance reinforcement control. Stability and robustness of the proposed method are theoretically analyzed. Simulations are implemented to illustrate the characteristics and performance of the proposed approach. Experiments with four healthy subjects are carried out to evaluate the effectiveness of the proposed method and show satisfactory results in terms of appropriate power assist and balance reinforcement. 

/pdf/impedance-modulation-control-of-a-lower-limb-exoskeleton-to-3571x7pw.pdf

Impedance Modulation Control of a Lower-Limb Exoskeleton to Assist Sit-to-Stand Movements

Hybrid FES-Exoskeleton Controller to Assist Sit-To-Stand movement

Pose-based features have demonstrated to outperform low-levelappearance features in human action recognition. New RGB-D cameras provide locations of human joints with which geometric correspondences can be easily calculated. In this article, a new geometric correspondence between joints called Trisarea feature is presented. It is defined as the area of the triangle formed by three joints. Relevant triangles describing human pose are identified and it is shown how the variation over time of the selected Trisarea features constitutes a descriptor of human action. Experimental results show a comparison with other methods and demonstrate how this Trisarea-based representation can be applied to human action recognition.

A New Relational Geometric Feature for Human Action Recognition

We aimed to biomechanically evaluate the effect of the supraspinatus tendon on tuberosity stability using two different reverse shoulder arthroplasty (RSA) models for complex proximal humeral fractures (PHFs). Four-part proximal humeral fractures were simulated in 20 cadaveric shoulders. Two different RSA designs were implemented: a glenosphere-medialized model and a glenosphere-lateralized model. Tuberosities were reconstructed, and displacement of bony fragments was measured (mm) by placing three sensors: in the humeral diaphysis (D), in the greater tuberosity (GT), and in the lesser tuberosity (LT). Axial forces were induced and measured in Newton (N). The test was performed twice in each specimen, with and without the supraspinatus tendon. The regression line (RL) was measured in mm/N. In the medialized model, the GT–D displacement was greater in the supraspinatus preserving model than that in the tendon excision model (p < 0.001), as well as for the LT–D displacement (p < 0.001). In the lateralized model, GT–D displacement and GT–LT distance were greater in the preserving model than that in the excision model (p < 0.001, p = 0.04). The supraspinatus tendon resection leads to a more biomechanically stable tuberosity construct when performing RSA for PHFs, while the rest of the rotator cuff tendons (infraspinatus and teres minor) are retained in the greater tuberosity. Basic science study. Cadaveric study.

/pdf/retaining-or-excising-the-supraspinatus-tendon-in-complex-2mmb7lc0z3.pdf

Retaining or excising the supraspinatus tendon in complex proximal humeral fractures treated with reverse prosthesis: a biomechanical analysis in two different designs

The concept and design of a friendly human-machine interaction system for an adapted robotized kitchen is presented. The kitchen is conceived in a modular way in order to be adaptable to a great diversity in level and type of assistance needs. An interaction manager has been developed which assist the user to control the system actions dynamically according to the given orders and the present state of the environment. Real time enhanced perception of the scenario is achieved by means of a 3D computer vision system. The main goal of the present project is to provide this kitchen with the necessary intelligent behavior to be able to actuate efficiently by interpreting the users' will.

Friendly human-machine interaction in an adapted robotized kitchen

The growing need of technological assistance to provide support to people with special needs demands for systems more and more efficient and with better performances. With this aim, this work tries to advance in a multirobot platform that allows the coordinated control of different agents and other elements in the environment to achieve an autonomous behavior based on the user’s needs or will. Therefore, this environment is structured according to the potentiality of each agent and elements of this environment and of the dynamic context, to generate the adequate actuation plans and the coordination of their execution.

/pdf/suitable-task-allocation-in-intelligent-systems-for-4pdtzxx35x.pdf

Suitable task allocation in intelligent systems for assistive environments

A real time approach to the recognition of human activity based on the interaction with objects in domestic settings is presented. It is based on capturing partial images where the activity takes place using a color camera, and processing the images to recognize the present objects and their location changes. For object description and recognition, a histogram on a normalized chromaticity space has been selected. The interaction with the objects is classified as four types of possible actions regarding change in location (add, remove, move or unchanged). Activities are defined as receipts, where objects plays the role of ingredients, tools or substitutes. Sensed objects and actions are then used to analyze in real time the probability of the human activity that is being performed at the moment in a continuous activity sequence. Tests are carried on a automated kitchen scenario where proactive assistance wants to be implemented taking into account the early recognition user activity.

Anticipating human activities from object interaction cues

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