The IMPaCT Cohort (ISCIII, Spain) is expected to collect biomechanical parameters from a wide population (~200,000) over seven consecutive days, using a triaxial accelerometer and a gyroscope positioned on both the wrist and thigh of participants.This will be one of the distinctive features of the Cohort, based on the hypothesis that simultaneous placement of two devices on the wrist and thigh will enable accurate classification of subjects' activity.In this study, we aim to explore this crucial aspect using Deep CNNs and data from publicly available datasets.Our experimental findings demonstrate an 85% accuracy achieved when utilizing data from both the thigh and wrist.The results support the hypothesis that incorporating accelerometry data from both limbs enhances classification, yielding over a 15% increase in accuracy compared to using data from a single limb alone.

This paper presents a complete design and validation of a specific case of a Human-Robot collaborative workspace consisting of a robot arm and a human operator working in a pick & place task in close proximity. The human motion monitoring is made with IMU-based wearables and optical markers, and the robot motion planning method considers safety distance criteria as prescribed in ISO/TS 15066. This case study is meant to illustrate the design of collaborative environments by using the Model-based Design approach. The goal is to systematically address the problems of safety assurance and performance optimization, allowing the use of optimization and machine learning approaches.

A tool used to capture biomechanical signals from the upper limb is presented here. The methods used for data acquisition, signal fusion and joint amplitude measurements, elbow flexion/extension and pronation/supination, are also presented. To that aim, a device using two inertial and magnetic sensors fixed to the arm and the forearm body segments was implemented. These sensors are wired to a control unit, which process them and sends the information to a display device through a wireless communication protocol. This process can be done in real time, or the results can be stored and managed later. This device was applied to an industrial robot arm, and the results were compared to the actual rotation values. Experiments showed a RMSE of 2.19° in flexion/extension and of 2.75° in pronation/supination. As a conclusion, it can be claimed that the system has an acceptable level of precision to be used as a support tool in rehabilitation processes of people with slight motor damages.

Obstacle detection is a critical task in mobile robot navigation. To allow for rapid reactions to changes in the environment, it is required to complete its operation in short time intervals. That can be difficult if computing and/or sensing robot resources are limited. Therefore, it is important to design precisely what sensorial information would be needed to guarantee detection, and how it would be gathered to guarantee throughput. In this paper we present analysis and experiments related to the design of an obstacle detection system for a mobile robot with limited sensing capabilities. Objectives are to preserve safety while maintaining good motion performance. The design is made in two steps. Firstly, to find what is the minimum information needed to assure safety as a function of the sensors at hand, the robot capabilities and the intended motion control policy. Second, to manage the available sensors in order to provide the required information when needed. For the example studied, a justified solution has been defined which guarantees detection, robot safety, and good motion performance in real time. The solution has been tested with experiments that illustrate the effectivity achieved.

Robot Navigation is an especially challenging problem when only online sensor information is available. The main problem is to guarantee global properties, such as algorithm convergence or trajectory optimality, based on local information. In this paper we present a new non-heuristic sensor-based planning algorithm, characterized by: 1) it is based in potential functions, allowing to introduce optimality criteria, 2) it is computed incrementally to introduce last sensor readings, and 3) it accounts for robot dynamics. The result is a method suitable for real-time navigation, it is intuitive and easy to understand, and produces smooth and safe trajectories.

In experimental robotics it is common to complement the motion planning algorithm with a local obstacle avoidance module. We are concerned here with the specifications of a range sensor specifically design for the task. Such sensor has special requirements: it has to guarantee detection and response, and it has to guarantee throughput, providing information ail a rate in proportion to robot velocity. An analysis is presented which allows the selection of the sensor requirements for collision avoidance tasks in mobile robots. The design takes into account robot dynamics, it is compatible with fast motion-only the indispensable environment zones are explored-and avoids unnecessary velocity reductions. Experiments show the possibilities and limitations of the approach.

The effect of robot dynamics on autonomous navigation is a key issue in sensor-based motion planning. In most of existing works, the solution is attempted by separating the planning and control into two sequential stages; as a minus, this may, for example, adversely effect the algorithm convergence. The strategy proposed in this paper solves the problem by combining motion planning and control within a single-stage procedure. The procedure exhibits good dynamic behavior, while providing safety (collision avoidance) and fast response. Results of testing the approach on a commercial Nomad-200 mobile robot are presented. Also discussed is the effect of model parameters on motion performance.