RESUMEN
The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human-robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.
Asunto(s)
Actividades Cotidianas , Mano , Humanos , Extremidad Superior , Aprendizaje Automático , PosturaRESUMEN
Different primate species have developed extensive capacities for grasping and manipulating objects. However, the manual abilities of primates remain poorly known from a dynamic point of view. The aim of the present study was to quantify the functional and behavioral strategies used by captive bonobos (Pan paniscus) during tool use tasks. The study was conducted on eight captive bonobos which we observed during two tool use tasks: food extraction from a large piece of wood and food recovery from a maze. We focused on grasping postures, in-hand movements, the sequences of grasp postures used that have not been studied in bonobos, and the kind of tools selected. Bonobos used a great variety of grasping postures during both tool use tasks. They were capable of in-hand movement, demonstrated complex sequences of contacts, and showed more dynamic manipulation during the maze task than during the extraction task. They arrived on the location of the task with the tool already modified and used different kinds of tools according to the task. We also observed individual manual strategies. Bonobos were thus able to develop in-hand movements similar to humans and chimpanzees, demonstrated dynamic manipulation, and they responded to task constraints by selecting and modifying tools appropriately, usually before they started the tasks. These results show the necessity to quantify object manipulation in different species to better understand their real manual specificities, which is essential to reconstruct the evolution of primate manual abilities.