Hello Everyone. I am Chen Liang from Tsinghua University.
It’s my pleasure to share my work - DualRing: Enabling Subtle and Expressive Hand Interaction with Dual IMU Rings.
In this work, I collaborate with Chun Yu, Yue Qin, Yuntao Wang, and Professor Yuanchun Shi.
DualRing features a ring form input device composed of two IMU rings and an impedance measurement circuit
to sense rich hand gestures and subtle finger movements.
The two rings are worn on the user’s thumb and index finger to sense the orientation and the movement of hand segments.
A high-frequency AC signal is transmitted through the fingers to detect on-body contact signals.
DualRing has 3 sensing purposes.
First, the AC circuit detects thumb-to-finger contacts, including whether a thumb-to-finger
touch happens and which finger the thumb is touching, to enable pinch-based interactions.
Second, the two IMUs give relative attitudes between the thumb and the index finger.
With a parameterized approximation, the relative attitude is mapped to the desired 2D coordinates
to enable cursor-based interactions.
Third, statistical features from the two IMUs can be extracted for gesture classification.
We will illustrate the technical details below.
We build an impedance measurement circuit to detect contact behavior.
The synthesizer first creates the reference signal and the monitored signal synchronously.
The monitored signal is passed through the human body,
transmitting through the 2 virtual paths, then multiplied with the reference signal.
We pass the multiplied signal through a low pass filter and
get the phase delay relevant value that indicates the pinch status.
When the thumb touches different fingers,
changes in the circuit component will lead to variations in signal amplitude.
For coordinates mapping, we compute the rotation matrix of each IMU and combine them to compute the relative attitude.
Then we model the mapping function in a relaxed linear form and
adopt least-square optimization with a regulation term to learn the parameters.
For gesture recognition, we extract two categories of features -
the statistics features and the relative features from the raw data.
Then we train a random forest model to classify the gestures.
Based on the sensing capability above,
we outline the interaction space and divide it into three sub-spaces:
within-hand interaction, hand-to-surface interaction, and hand-to-object interaction.
Within-hand interaction includes gestures between the thumb and the other four fingers,
such as pinching, continuous cursor control, region selection, sliding, and pattern drawing.
Hand-to-surface interaction includes on-surface detection, surface gestures, for example, clicking with different fingers,
and the combination of dominant gestures and controlling gestures.
Hand-to-object interaction includes gripping state recognition,
such as recognizing which object the user is gripping, and interaction on the object,
such as clicking on a gripped object.
We conducted a study to evaluate DualRing’s performance in recognizing different hand gestures in the three sub-design spaces.
14 participants were asked to record 31 gestures from 6 representative gesture sets.
Then we evaluate DualRing’s performance in each gesture set.
Generally, DualRing had a good performance in pinching recognition, swiping recognition,
segment selection, and detecting contact, tapping, and gripping gestures.
Results also demonstrate the informational advantage of DualRing
in sensing comprehensive hand gestures compared with single-ring-based solutions.
Our usability study showed that the interaction spaces enabled by DualRing
are favored by users for their usability and novelty.
There are several issues related to the form factor, the interaction space design,
and the motivation of DualRing, which are detailedly discussed in our paper.
That’s all for my presentation. Thank you for your attention.