Augmented Tai-Chi Chuan Practice Tool
with Pose Evaluation
1

Yao-Fu Jan, 2 Kuan-Wei Tseng, 2 Peng-Yuan Kao, 1,2 Yi-Ping Hung
Department of Computer Science and Information Engineering,2
Graduate Institute of Networking and Multimedia,
National Taiwan University, Taipei, Taiwan
hung@csie.ntu.edu.tw

1

Abstract—Tai Chi Chuan (TCC) is a well-known Chinesemartial art that promotes health. In addition to learning TCC
from a coach in a classroom setting, learners usually use books
or videos to practice on their own. However, since turning isa frequent movement in TCC, learners cannot watch a tutorial
and practice TCC at the same time. Furthermore, it is difficult
for users to determine whether their postures are correct. We
propose an augmented reality TCC practice tool with pose
evaluation to help people practice TCC on their own. The
tool consists of an optical see-through head-mounted display,external cameras, digital compasses, and a server. Users learn
TCC movements from surrounding virtual coaches in augmented
reality and determine whether their postures are correct via anevaluation module. Study results show that the proposed tool
provides a helpful learning environment for TCC and that thepose estimation and evaluation are robust and reliable.
Index Terms—Tai-Chi-Chuan, Sport Learning, Pose Estimation, Augmented Reality, Human Computer Interaction

I. I NTRODUCTION
Many studies show that Tai Chi Chuan (TCC) improves
health both physically and mentally [1]–[5]. Usually, TCC
is learned from a coach in a class setting. As coaches are
not available outside of class time, people typically consult
instruction books or watch demonstration videos to practice
TCC on their own.
Nevertheless, there are several drawbacks to such books andvideos. First, people generally see the coach’s posture from a
specific, fixed point of view; they cannot change to whatever
point of view would be better. Second, as some TCC movements involve turning the head, checking one’s posture in sucha case obliges the student to stop performing the movement
before consulting the instruction book or demonstration video,
which interrupts their progress. Last, it is difficult for users todetermine whether their postures and movements are correct.
Although looking into a mirror is a solution, few users have theluxury of a dance classroom environment. Thus the perspective
or occlusion problem still exists.
To help people practice TCC on their own, we propose
a TCC practice system consisting of a learning module andan evaluation module. In the learning module, users wear an
optical see-through head-mounted display (OST-HMD) andsee virtual coaches surround them in augmented reality (AR).
Fig. 1 shows the architecture of the proposed system. AR andthe HMD allow the user to simultaneously observe coaches

Fig. 1. Scenario drawing and the system architecture of the proposed
augmented Tai-Chi Chuan practicing toolfrom multiple perspectives and practice the movement without
distractions or interruptions. The evaluation module helps
users to determine whether their movements or postures arecorrect. With digital compasses attached to the feet and external camera(s) continuously monitoring users’ feet orientationand capturing imagery, we reconstruct the user skeleton at theserver and compare it with the correct movements to evaluate
their performance. We summarize the key functionalities ofthis work:• Virtual Coach: The user inspects and imitates coachmovements from multiple perspectives to practice TCC
in augmented reality. (Section III.A)• Movement Learning: To aid users when practicing continuous movements, we label all key postures in every
movement. (Section III.B)• Pose Estimation: We analyze color images from thecamera and digital compass measurements to reconstruct
the 3D human pose with feet orientation. (Section IV.A)• Pose Evaluation: To highlight differences between theuser’s pose and that of the coach, we align and compare
the postures, output the corresponding scores, and offer
video for review. (Section IV.B)
The contributions of this work are threefold. First andforemost, we propose a versatile augmented TCC learning
module. We create virtual coach of Yang-Style TCC whichsurrounds the user in AR based on real TCC coach’s movement. Secondly, we propose a pose evaluation module to help
user improve their practice. We design a novel grading rubrics
along with review interface to remind users of noteworthy
actions. Last but not least, from our user experience study, weinvestigate user preference on the layout of virtual coaches andinteraction method in AR system, which serve as reference forsubsequent development of AR sport learning system.
II. R ELATED W ORK
A. Sport Learning Systems
When learning or practicing sports at home, a flat display such as a television or projector is the most common
multimedia. MotionMA [6] and YouMove [7] use Kinect toanalyze and model experts’ motions. Comparison to student
movement yields feedback information that is shown on thescreen. Physio@Home [8] uses a wearable device and two
cameras that view the user from different angles. Via guidance
information in front of a TV, the system decreases the numberof mistakes when physiotherapy patients do their exercises
through observation from different angles. SleeveAR [9] also
shows the potential of projector-based guiding systems in guiding user hand movements. Recent years have seen increased
use of OST-HMDs. Stylo-Handifact [10] consists of a haptic
device attached to the forearm and a visualization of a virtual
hand, which are combined to provide visuo-haptic feedback
for posture training applications. Ikeda et al. [11] and Yeo et
al. [12] use OST-HMDs for self-paced sport training to help
the user imitate the expert’s motions.

Fig. 2. Flow of user interaction for learning module

To evaluate pose correctness, we measure the pose similarity
between the user and the coach. This has been measured byjoint positions’ Euclidean distance [7], [24], or in terms of
the base of joint angles [6], [25]. Measuring similarity basedon joint angles rather than joint positions removes the needfor calibration procedures to eliminate the error caused bydifferences between users’ body shapes.

B. Tai Chi Chuan Learning Systems
Chen et al. [13] use pressure-sensing insoles for learning
TCC. They use a smartphone or tablet to show visual feedbackon the weight distribution of the user’s feet as well as thevirtual coach’s feet. “Tai Chi in the Clouds” [14] uses a micro
unmanned aerial vehicle (UAV) as “clouds” to lead or follow
hand movements, providing live feedback on smoothness of
movement via LEDs. Chua et al. [15] propose a training
system for TCC based on a VR headset to guide body
movements using virtual coaches. Users watch their bodyinformation in the virtual world via an avatar reconstructedfrom their skeleton animation. Whereas the system proposedby Iwaanaguchi et al. [16] is similar to that of [15], but theirsystem only contains the virtual coach’s movements.
In previous work [17], [18] we use an OST-HMD to learn
TCC. Users easily evaluate their movements in a virtual mirror
shown on the OST-HMD when practicing TCC. We also
use wearable devices to detect wrist motion [19] and feetpressure [20], [21], which helps users to modify their bodypose more effectively.
C. Pose Estimation and Evaluation
OpenPose [22] is a well-known real-time pose estimation
method to detect multiple people’s 2D poses from a single
RGB image. It is a bottom-up method, which means that it
detect joints first and then connects the joints to form theskeleton of the human body. Lifting from the Deep [23] is a
deep architecture for detecting 2D and 3D pose from a single
RGB image. It uses a four-stage architecture which optimizes
2D and 3D poses together to yield a refined 2D and 3D pose
after a few iterations.

III. L EARNING M ODULE
In this section, we introduce the augmented reality TCC
learning module of the proposed system. Fig. 2 shows theinteraction flow of the learning module. The only hardware
is the Microsoft HoloLens (first generation), a mixed-reality
head-mounted display running the Windows Mixed Reality
platform, which is equipped with multiple sensors, including
inertial measurement units (IMUs), environment understanding
cameras, a depth camera, and so on, which contribute to its
robust environment localization and mapping.
A. Virtual Coach(a) Movement recording(b) Animation creation

Fig. 3. Virtual coach creation

1) Virtual Coach Creation: There are many styles of TCC.
In this work, we invited a TCC master to demonstrate standard
Yang-Style TCC. His movements were recorded by a Vicon
motion capture system consisting of 54 Vicon T160 cameras toform a capture volume of 15m (L) × 5m (W) × 3m (H). The
movements recorded from the TCC master were first converted
to the animation file and then imported into Unity 3D as theFig. 4. Virtual coach layout in AR

Fig. 5. Hidden panel in learning module

animation of the virtual coaches in our system. Fig. 3 shows
the creation of the virtual coach.
2) Virtual Coach Layout: The user is surrounded by eight(this is customizable) virtual coaches in the augmented environment, as shown in Fig. 4. Users observe the motion not
only of coaches in front of them but also of the coaches behindthem. This solves the fixed perspective and occlusion problems. In addition, twelve numbers surround the user. These
numbers are the direction prompts showing the clock position.
Each movement starts from the twelve o’clock direction. The
virtual coaches’ orientations and the twelve numbers are set
to the orientation of the user’s head when the user enters
the learning module, and can be changed by the user. While
practicing, the relative distance between the virtual coaches
and the user remains constant. However, since the horizontal
and vertical field of view (FOV) of Microsoft HoloLens are
restricted to 30 and 17.5 degrees respectively, we shrink thesize of the virtual coaches to fit the FOV, which makes itslightly less realistic.
3) Configuration Setting: Here the user configures theenvironment. Users can adjust the demonstration speed andthe mounting height of the virtual coach. The default (normal)
speed is as fast as the TCC master. Users can adjust the speed
according to their learning status. As for height adjustment,
since user head shapes differ, the viewing angle varies slightly
when wearing the HMD. Thus, the preset height of the virtual
coach may not apply to all users. Users use the up and down
buttons to adjust the height of the virtual coach positions.

and key posture are shown in the top-left corner of the FOV
in the HMD. Next, the user is surrounded by virtual coaches,
whose spatial layout was specified in the previous section. All
the coaches repeatedly demonstrate the movements that user
has selected, while the system first speaks the name of the
movement and then speaks every key posture as the coaches
demonstrate it. The user follows the instructions and imitates
the coaches’ posture. If the user feels difficult in continous
playing mode, he/she can enter PKP mode to practice a certain
key posture. In this mode, coaches demonstrate the same
movement but pause at each key posture for the user to learn
and observe. For instance, there are three key postures in Wave
hand like cloud (分勁雲手): Unfolding palm (撐手), Left wave
hand (左雲手), and Right wave hand (右雲手). If the userpractices this movement in PKP mode, the coaches pause at
Unfolding palm before the user switchs to the next key posture.
2) Sequential Movement Learning: Connection between
each movement is vital in TCC, and thus experienced TCC
practitioners prefer sequential movement. They are familiar with all TCC movements and tend to practice different
movements continuously without interruptions. In sequential
movement learning, the user either starts from the first TCC
movement, i.e., Preparation (太 極 起 式) or specifies any
desired movement to start with. As with the single movementmode, the user is surrounded by virtual coaches who demonstrate the selected movements sequentially once. During thedemonstration, the user can still switch to the next or previous
movement.

B. Key Functionalities

C. User Interaction

There are two primary learning modes in the proposed
learning module: single movement learning and sequential
movement learning. Specifically in single movement learning,
there are two playing modes: continous playing mode andpause-at-key-posture (PKP) playing mode. User can choose
the appropriate mode according to their own learning status.
1) Single Movement Learning: There are 108 movements
in Yang-Style TCC, each of which is composed of several key
postures with distinctive names. In single movement learning,
the user practices either key postures or a single movement
in a continuous manner. After entering this mode, the user
first selects one movement by name to practice from the
movement selection menu. The name of the mode, movement,

To enhance the user experience, we provide three interaction
methods: hidden panel, hot key, and voice command. In single
or sequential movement learning, there are several buttons in
the hidden panel, as shown in Fig. 5. These are invisible during
practice but can be revealed by an air tap, which is a HoloLens
gesture. The user can also control the system with the hot
key, which can be triggered by a HoloLens gesture or thephysical clicker. Generally speaking, the user single-clicks tomove to the next movement or key posture, double-clicks toreturn to the parent node, and triple-clicks to move back to
the previous movement or key posture. The user can also readout the command name to manipulate the system, for instancesaying stop to pause the demonstration or play to resume.

Fig. 6. Evalution module layout. (We support one or multiple cameras.)

Fig. 7. Hardware and communication (transmit via MQTT protocal)

IV. E VALUATION M ODULE

into the pretrained probabilistic model to lift the 2D pose into
a 3D pose. Then we extract the elbow angles θˆ0 LE , θˆ0 RE andknee angles θˆ0 LK , θˆ0 RK from the generated 3D pose.

In this section, we describe the evaluation module of the
proposed system, both hardware and software. Fig. 6 shows
the layout of the module: it consists of external cameras, a pairof special shoes equipped with a digital compass and singleboard computers, and a remote server, as shown in Fig. 7.
Image and measurement data are sent to the server, where wereconstruct the human pose and calculate the similarity to thecoach’s movement, which is taken as the ground truth for thescores and review video.
A. Digital Compasses
We installed a 3-Axis Magnetic Sensor QMC5883L and a
Raspberry Pi single-board computer on a traditional kung-fu
shoe. The Raspberry Pi receives the yaw angle measurements
for both of the user’s feet θ̂LF and θ̂RF at 5 Hz from thedigital compasses and sends these data to the server wirelessly.
To eliminate orientation bias between the learning systemand the digital compass, we take the mean value of the yaw
angle measurements as the base number and subtract eachmeasurement from their mean to get the relative differenceθˆ0 LF and θˆ0 RF . We do the same with the ground-truth value00
to get θLF
and θRF.
B. Pose Estimation with External Cameras
In pose estimation, we reconstruct the human pose byanalyzing imagery from the external camera. If there are
multiple cameras, we select one optimal perspective and discard the others. The image frames captured from the optimal
perspective are fed as input to reconstruct the human pose.
We estimate the 2D pose and use this to infer the 3D pose.
For 2D pose estimation, we use OpenPose [22], a well-known
bottom-up pose estimation method which detect joints, which
are then connected to form the skeleton of the human body.
First, it takes an input color image to estimate part confidence
maps and part affinity fields (PAFs). A part confidence map
describes the confidence of the locations of the human joint
positions, whereas PAFs describe the relationship between
each detected human joint. Then, OpenPose uses bipartite
matching to connect the parts (joints) to form the skeleton.
After acquiring the 2D pose, we follow Lifting from the
Deep [23] for 3D pose estimation: we feed the 2D human pose

C. Pose Evaluation
We align the measurement and ground-truth data via dynamic time warping (DTW) and measure the similarity between two sequences with the Gaussian function. The output
is a score along with a review video, as shown in Fig. 10.
We color correct, acceptable, and incorrect postures as green,
yellow, and red so that the user can use the color to determine
whether his/her posture is correct.
1) Dynamic Time Warping (DTW): Dynamic time warping is a fundamental algorithm for similarity measurements between two unequal-length signals. Let G =(g1 , g2 , . . . , gn ) and U = (u1 , u2 , . . . , um ) denote the discrete time signal whose lengths are n and m respectively. The objective is to find a warping path W ={(pi , qi ) | 1 ≤ i ≤ k, k = min(m, n)} such that the warping
cost D is optimized by dynamic programming:
D = minWkX

kG(pi ) − U(qi )k2

(1)

i=1

2) Similarity Measurement: Although the cumulative Euclidean distance calculated in DTW can represent the similarity between two sequences, it is not normalized. Hence, weapply a Gaussian function-based similarity metric to compare
the aligned signal askG(pi ) − U(qi )k2
),
(2)
2σ 2
where a is the normalization factor and σ is the Gaussian RMS
width, which represents the tolerance of error.
3) Grading Rubric: Our grading rubric has three criteria:
absolute elbow angles Selbow , absolute knee angles Sknee , andrelative foot angles Sfoot . For each criteria, the score is calculated by Eq. 2. Note that we calculate the left side and rightside separately. If the score of a certain criteria is less than40, we color it red in the review video, implying the postureis incorrect. If the score lies between 40 and 60, we color it
yellow, indicating acceptable posture. If the score is higher
than 60, we color it green, showing the posture is correct. The
Si = a exp(−

(a) Poor performance(b) Good performanceFig. 8. User interface of review system (green: correct; yellow: acceptable; red: incorrect)

total score, which represents the overall performance of theuser, is the weighted sum of each criteria:
X
Stotal =wi Si(3)i ∈ {elbow, knee, foot}where wi and Si denote the weight and score of each criteria.

TABLE IS ELECTED YANG -S TYLE TCC MOVEMENTS
Movement
Wave hand like cloud
Grasp bird’s tailRight brush knee,
twist stepArmmovement
X
X

Body
rotation

X

X

Foot
movement

X
X

V. L EARNING M ODULE U SER E XPERIENCE
A. Interaction Methodology
From the user’s perspective, voice commands are convenient. Nevertheless, the built-in voice recognition system of
the HoloLens (1st gen) is not very robust. It tends to fail due
to environmental noise and user accents. Thus, the other two
interaction methods—the hidden panel and the hot key—are
indispensable. The hot key is triggered by physical clickers
or HoloLens gestures. The user can single-click, doubleclick, or triple-click to manipulate the system. The hidden
panel, in turn, contains several function buttons and can berevealed by an air tap. Each method has its advantages anddisadvantages. In this part, we describe a user study conducted
for comparison.
1) Experiment Procedure: Participants experimented withthe two interaction methods with the HoloLens (1st gen).
Before they started, we explained how the methods work. Thenthey followed our instructions to try out the functions in thelearning module, after which they rated the operation methods
and the functions using 7-point Likert scales.
2) Participants: Thirty six participants, including 23 females and 13 males, age 19 to 32, mean age 20.69 (SD =2.19), were recruited for the study. Most participants comefrom Tai-Chi Chuan Beginning Class.
3) Result: Fig. 9(a) shows that the hidden panel was
judged significantly better than the hot key in terms ofContinuous/PKP(Pause-at-Key Posture) Playing Mode Switch(hotkey: mean = 4.75, SD = 1.11; hidden panel: mean = 5.00,
SD = 1.39; one-tailed p = 0.40), Manipulation in PKP Mode(hotkey: mean = 4.00, SD = 1.60; hidden panel: mean =5.17, SD = 1.50; one-tailed p = 2.158E-3), and Manipulationin Sequential Movement Learning (hotkey: mean = 3.89, SD= 1.51; hidden panel: mean = 5.17, SD = 1.42; one-tailed
p = 4.31E-4). Participants commented that “triple-click isextremely difficult to access” (eighteen users) and “it takes
times to memorize all hot key operation.” (nine users).
B. Overall Satisfaction
We also asked the participants about the overall user experience. “Usefulness of virtual coach (virtual coach)”, and threemodes “Continuous playing mode in Single Movement Learning (continuous)”, “PKP mode in Single Movement Learning(PKP)”, and “Sequential Movement Learning (sequential)” areevaluated. Most participants thought highly of the proposed
learning module, as shown in Fig. 9(b). Although most weresatisfied, some still suggested improvements: “it would bebetter to allow users to modify the orientation of coach”(eight users), “the buttons on hidden panel should be more
centralized.” (six users).
VI. E VALUATION M ODULE U SER E XPERIENCE
A. Utility of Evaluation Module
To verify the effectiveness of the proposed evaluation module, we invited five advanced TCC students to participate inuser studies. Since it is unreasonable to ask TCC beginners
or people who do not practice TCC to evaluate the pose
estimation results, we could only depend on a TCC masteror an experienced TCC practitioner. We selected three representative movements in Yang-Style TCC: Wave hand likecloud (分勁雲手), Grasp bird’s tail (攬雀尾), and Right brushknee, twist step (右摟膝拗步). The difference between these
movements is summarized in Table 1. These three movements
employ different motion styles and represent different levelsof difficulty, so we can evaluate the module’s pose estimationand feet orientation estimation separately.

(a) Comparison between hot key and hidden panel for learning module(b) User preference for each components in learning module

Fig. 9. User experience on learning module (36 participants; all of them are TCC beginners)(a) Comparison on grading rubrics (w/wo feet orientation)(b) Evaluation module user preference

Fig. 10. User experience on evaluation module (5 participants; all of them are experienced TCC practitioners)

1) Experiment Procedure: Before the experiment started,
to familiarize the participants with the equipment, they wereasked to wear the special shoes and HoloLens to practice
TCC several times. Afterwards, they were asked to practicethe selected TCC movements. During this practice, they wereto asked to perform the movement incorrectly to check whether
the evaluation module highlighted the erroneous pose.
2) Participants: Five male participants aged 24 to 26 witha mean age of 24.6 (SD = 0.89) were recruited for the study.
All participants had experience learning TCC: one for 6 years,
three for 5 years, and one for 4 years.
3) Result: We compared the scores using only the camera versus combining the camera and the digital compasses.
Fig. 10(a) shows that for movements without any feet movement, the scores are higher with feet orientation. This resultsfrom the total score is the weighted sum of three criteria. Users
can get nearly full scores of the relative foot angles Sfoot bystanding in place. However, for difficult movements with feetmovements, the scores dropped. This seems to indicates thatfeet orientation is easily ignored when practicing strenuous
movements. Hence, our grading rubric reminds the user tofocus more on feet orientation.
B. User Preference
Next, participants were asked to use a 7-point Likert scale
to rate the operability of the practice review user interface

and to judge whether the evaluation module helped themto modify their movements. Also, participants were asked
whether they would recommend TCC beginners to practice
TCC on their own with our system. The user preferences are
displayed in Fig. 10(b). Experienced TCC students agreed that
the Evaluation Module clearly represented coach movements,that the practice review UI was easy to use, and that themodule correctly identified errors and correct poses. Also,
they believed that the proposed module would satisfy theneeds of TCC beginners when memorizing TCC movements.
One participant, however, commented that “This module did
not clearly explain the reasons for errors and did not give
directions on how to improve” (P4).
VII. C ONCLUSION
We present an augmented Tai Chi Chuan practice tool withthe following contributions: a learning module that provides
labeled key postures and a user-friendly interaction method;an evaluation module which helps users to review and correct
their movements; an probe into AR interaction methodology
from user study. Our system architecture can also be applied toother sports that require close attention to posture. Despite thatmost users were satisfied with the system, we will continue toexplore how to effecrtively guide the user to correct posture.

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