Wearable sensing solutions have emerged as a promising paradigm for monitoring human musculoskeletal state in an unobtrusive way. To increase the deployability of these systems, considerations related to cost reduction and enhanced form factor and wearability tend to discourage the number of sensors in use. In our previous work, we provided a theoretical solution to the problem of jointly reconstructing the entire muscular-kinematic state of the upper limb, when only a limited amount of optimally retrieved sensory data are available. However, the effective implementation of these methods in a physical, under-sensorized wearable has never been attempted before. In this work, we propose to bridge this gap by presenting an under-sensorized system based on inertial measurement units (IMUs) and surface electromyography (sEMG) electrodes for the reconstruction of the upper limb musculoskeletal state, focusing on the minimization of the sensors’ number. We found that, relying on two IMUs only and eight sEMG sensors, we can conjointly reconstruct all 17 degrees of freedom (five joints, twelve muscles) of the upper limb musculoskeletal state, yielding a median normalized RMS error of 8.5% on the non-measured joints and 2.5% on the non-measured muscles.

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The characterization of human upper limb kinematics is fundamental not only in neuroscience and clinical practice, but also for the planning of human-like robot motions in rehabilitation and assistive robotics. One promising approach to endow anthropomorphic robotic manipulators with human motion characteristics is to directly embed human upper limb principal motion modes at joint level, which are computed through functional analysis, in the robot trajectory optimization. This planning method poses some challenges when the kinematics of the manipulator is different from the model used for human data acquisition. In a previous work, we proposed to tackle this issue by mapping human trajectories onto robotic systems relying on Cartesian impedance control. An alternative method could move from the application of the functional analysis to human upper limb kinematics, working directly in the Cartesian domain. In this work, we present the results of this characterization on the data from 33 healthy subjects during the execution of daily-living activities. We found statistical differences between the amount of variability explained by a given number of basis elements in different directions of the Cartesian space. This suggests that some directions of the space are associated with a more complex motion evolution with respect to others, opening interesting perspectives for robot planning, neuroscience and human motion control.