Recording human gestures from a wearable sensor produces valuable information to implement control gestures or in healthcare services. The wearable sensor is required to be small and easily worn. Advances in miniaturized sensor and materials research produces patchable inertial measurement units (IMUs). In this paper, a hand gesture recognition system using a single patchable six-axis IMU attached at the wrist via recurrent neural networks (RNN) is presented. The IMU comprises IC-based electronic components on a stretchable, adhesive substrate with serpentine-structured interconnections. The proposed patchable IMU with soft form-factors can be worn in close contact with the human body, comfortably adapting to skin deformations. Thus, signal distortion (i.e., motion artifacts) produced for vibration during the motion is minimized. Also, our patchable IMU has a wireless communication (i.e., Bluetooth) module to continuously send the sensed signals to any processing device. Our hand gesture recognition system was evaluated, attaching the proposed patchable six-axis IMU on the right wrist of five people to recognize three hand gestures using two models based on recurrent neural nets. The RNN-based models are trained and validated using a public database. The preliminary results show that our proposed patchable IMU have potential to continuously monitor people’s motions in remote settings for applications in mobile health, human–computer interaction, and control gestures recognition.

Anthropomorphic robotic hands are designed to attain dexterous movements and flexibility much like human hands. Achieving human-like object manipulation remains a challenge especially due to the control complexity of the anthropomorphic robotic hand with a high degree of freedom. In this work, we propose a deep reinforcement learning (DRL) to train a policy using a synergy space for generating natural grasping and relocation of variously shaped objects using an anthropomorphic robotic hand. A synergy space is created using a continuous normalizing flow network with point clouds of haptic areas, representing natural hand poses obtained from human grasping demonstrations. The DRL policy accesses the synergistic representation and derives natural hand poses through a deep regressor for object grasping and relocation tasks. Our proposed synergy-based DRL achieves an average success rate of 88.38% for the object manipulation tasks, while the standard DRL without synergy space only achieves 50.66%. Qualitative results show the proposed synergy-based DRL policy produces human-like finger placements over the surface of each object including apple, banana, flashlight, camera, lightbulb, and hammer.

Autonomous object manipulation is a challenging task in robotics because it requires an essential understanding of the object’s parameters such as position, 3D shape, grasping (i.e., touching) areas, and orientation. This work presents an autonomous object manipulation system using an anthropomorphic soft robot hand with deep learning (DL) vision intelligence for object detection, 3D shape reconstruction, and object grasping area generation. Object detection is performed using Faster-RCNN and an RGB-D sensor to produce a partial depth view of the objects randomly located in the working space. Three-dimensional object shape reconstruction is performed using U-Net based on 3D convolutions with bottle-neck layers and skip connections generating a complete 3D shape of the object from the sensed single-depth view. Then, the grasping position and orientation are computed based on the reconstructed 3D object information (e.g., object shape and size) using U-Net based on 3D convolutions and Principal Component Analysis (PCA), respectively. The proposed autonomous object manipulation system is evaluated by grasping and relocating twelve objects not included in the training database, achieving an average of 95% successful object grasping and 93% object relocations.