Muscle Synergy Network during Nursing Care
Robot-Assisted Sit-to-Stand Transition
Tianyi Wang*
Institute for Multidisciplinary Sciences
Yokohama National University
Yokohama, Japanwang-tianyi-yf@ynu.ac.jp
ORCID 0000-0001-8066-6408

Yuko Ohno

Keisuke Shima

Graduate School of Medicine Faculty of Environment and Information Science
Yokohama National University
Osaka University
Yokohama, Japan
Osaka, Japan
shima@ynu.ac.jp
ohno@sahs.med.osaka-u.ac.jp
ORCID 0000-0002-6206-8663
ORCID 0000-0002-3469-6793

Abstract—This study aims to reveal the muscle synergy-based
network when sit-to-stand (STS) was performed with and without
nursing care robot assistance. Six subjects participated in the experiment. They performed the STS transition by themselves andwith robot assistance. Eight muscular activities were measured.
Muscle synergy was extracted using non-negative matrix factorization. An extended Bayesian Information Criterion graphicallasso network was constructed and analyzed. As a result, adifferent network of muscle synergy during STS transition wasobserved.
Index Terms—Sit-To-Stand Transition, Nursing Care Robot,
Muscle Synergy, Network Analysis

I. I NTRODUCTION
Sit-to-stand (STS) transition is an important movement inour daily life and is identified as the most difficult andmechanically demanding activity. However, due to various
physical disabilities, people who need support with the STS
are increasing. Thus, various STS assistance robots were
designed to solve this problem [1].
Because when STS is assisted by a nursing care robot,
the robot directly interacts with its users, whether the robot
could meet the need depends on the user’s performance. The
evaluation of the STS robot’s effect on humans from a different
viewpoint is necessary. In our previous study, the STS nursing
robot was evaluated from the center of press, center of mass,
motion coordination, and muscle synergy [2]–[5].
Considering that the STS-related muscles do not work
independently, and there are different types of muscle synergy
during the robot-assisted STS, it is hypothesized that there
exists a particular network between different muscle synergy
pattern. As a consequence, this study aimed to reveal themuscle synergy-based network when STS was assisted by anursing care robot. Findings from this study can be expected
to provide deeper insight into human-robot interaction fornursing robotics.
II. M ETHODS
Six healthy young volunteers (age: 25.8 ± 2.5 y.o., height:
1.78 ± 0.02 m, body mass: 72.0 ± 8.4 kg) participated in* Corresponding Author: Tianyi Wang.
E-mail: wang-tianyi-yf@ynu.ac.jp

Fig. 1. Overview of nursing care robot for STS assistance.

the experiment. None of them reported lower limb pathology,
neurological disease, low back pain, or use of medication that
may influence motor ability. This study was approved by theEthics Committee on the Division of Health Science, Graduate
School of Medicine, Osaka University (No. 305, 20140821).
Fig. 1 (a) illustrates the overview of a STS nursing carerobot. This robot mainly consisted of four parts: a seat, fourbar links, a motor, and a bottom base [5]. STS can be supportedby the robot seat’s vertical and rotation movement. Trajectoriesof three markers on the robot seat are plotted in Fig. 1 (b).
Fig. 1 (c) and (d) depict the prototype of the robot and one
example of STS supported by the robot.
Fig. 2 depicts the experiment and measured muscles. To
compare the muscle synergy of STS with and without robot
support, two experimental conditions were set. We asked allvolunteers to sit on the robot with a 43 cm seat height of
80° between crus and feet and performed STS without robot
support (Self-STS) and with robot support (Robot-STS) forFig. 2. STS experiment overview.
Fig. 4. Results of muscle synergy network.

III. R ESULTS AND D ISCUSSION

Fig. 3. Results of muscle synergy.

both five times.
Eight muscular activities at RA (upper rectus abdominis),ES (erector spinae), RF (rectus femoris), VASL (vastus lateralis), BFL (biceps femoris long head), TA (tibialis anterior),
SOL (soleus), GASL (gastrocnemius lateral head) were measured through surface electromyogram (sEMG). Muscle synergy was extracted from the sEMG using non-negative matrix
factorization. Variance accounted for (VAF) was calculated
to determine the number of synergies. The spatial pattern ofmuscle synergy was later used for network analysis.
To reveal the network between muscle synergy, an extended
Bayesian information criterion graphical lasso network analysis with 200 non-parametric bootstraps was performed. The
tuning parameter λ was set at 0.5. In the network, the spatialsynergy pattern was presented by nodes, which were connectedby edges that indicate the association between each node.
Difference among spatial pattern of muscle synergy, andthe VAF was analyzed using JASP (version 0.16.0.0). Nonparametric one-way and two-way ANOVA were used to ascertain the presence of a significant difference in VAF andmuscle synergy. Dun’s post hoc comparison was tested. The
significant level was defined to be 5%. Network modeling andanalysis were performed using JASP network analysis.

Fig. 3 depicts the results of muscle synergy. There weretwo synergies for self-STS and three synergies for Robot-STS.
The spatial muscle synergy showed a significant difference
between Self- and Robot-STSs, expected for GASL and ES.
Fig. 4 depicts the muscle synergy network results. For
both Self-STS and Robot-STS, network weight between SOL
and GASL had the highest value, which indicates nodes 1
and 3 hold the strongest positive correlation (blue and thicklines). TA muscle at Self-STS synergy one and two Robot-STSsynergies showed the highest negative correlation between RA(red and thick lines).
The experimental results verified our hypothesis: STSrelated muscles worked with coordination and there were particular networks among those muscles. This is the first study
that focused on the muscle synergy network during nursing
care robot-assisted STS. There are still some limitations inthis study. We did not take temporal muscle coordination into
consideration, and we did not test the nursing care robot on theolder citizens or any patients. Further analysis of the network,e.g. the centrality and similarity are necessary.
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