Abstract
Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness.
Highlights
The human hand is the primary tool to interact with the external world
In our recent works [9,10], we focused on the development of unobtrusive and wearable interfaces based on textile goniometers (knitted piezoresistive fabrics (KPF) technology) for monitoring human hand activity in daily life
This paper presents a sensing glove based on KPF sensors
Summary
The human hand is the primary tool to interact with the external world. Given its fundamental role in daily life activities, it is not surprising that much effort has been put into measuring its kinematics, with the two-fold goal of understanding the underpinning neuroscientific control mechanisms and to develop effective human-machine interfaces, with promising applications in rehabilitation, virtual reality, musical performance, video games, teleoperation and robotics [1,2].accurate kinematic recordings through hand pose reconstruction systems (HPR)are difficult to achieve, due to the large number of degrees of freedom (DOFs) involved along a continuously-compliant structure. Accurate kinematic recordings through hand pose reconstruction systems (HPR). HPR systems common in the state of art can be divided into remote and wearable systems (see [1,3] for a survey). The former category comprises systems that are mostly visual based, while in the latter fall in glove-based systems. While visual-based HPR systems are inexpensive, unobtrusive and typically very usable, their reconstruction capabilities are still far from an effective usage for continuous monitoring of daily life activities, since they are usually not mobile
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