![](bg6.png)
be segmented and normalized using elbow flexion and
shoulder flexion as reference to identify each part of
the protocol.
Figure 7: Graphics of Left Shoulder Flexion and Left Elbow
Flexion.
The figure 7 shows raw data from shoulder and
elbow of a test subject. Both graphs shows clearly
that the protocol starts around the 750th time sample
(sample rate of 100Hz) because both shoulder and el-
bow angles have a significant variance starting in this
timestamp. This pattern is repeated for each spot, in-
dicating that is possible to identify when the patient is
reaching each spot. Hence, it’s possible to calculate
maximum and minimum values for flexion angles.
4 CONCLUSIONS
In conclusion, the preliminary study has brought
some evidences that could lead on a innovative treat-
ment for tetraplegics. A few improvements such as
setup and an appropriate support for the controller
should be made before the VR environment is used
with SCI subjects. However, the main mechanics of
the VR environment worked which may suggest that
it could work with SCI. The next steps are: a) Create a
baseline calibration for VR environment; b) Polish the
3D models to create a better immersive experience;
c) Ethics committee approval for VR experiments on
SCI subjects; d) Validate the VR Environment with a
SCI population.
ACKNOWLEDGEMENTS
This work was supported by grants from S
˜
ao Paulo
Research Foundation (FAPESP) and from National
Council for Scientific and Technological Develop-
ment (CNPq)
REFERENCES
An, C. M. and Park, Y. H. (2018). The effects of semi-
immersive virtual reality therapy on standing bal-
ance and upright mobility function in individuals with
chronic incomplete spinal cord injury: A preliminary
study. Journal of Spinal Cord Medicine, 41(2):223–
229.
Bergmann, M., Zahharova, A., Reinvee, M., Asser, T.,
Gapeyeva, H., and Vahtrik, D. (2019). The effect of
functional electrical stimulation and therapeutic exer-
cises on trunk muscle tone and dynamic sitting bal-
ance in persons with chronic spinal cord injury: A
crossover trial. Medicina (Lithuania), 55(10).
Devivo, M. J. and Chen, Y. (2011). Epidemiology of trau-
matic spinal cord injury. In Spinal Cord Medicine:
Second Edition.
Dimbwadyo-Terrer, I., Trincado-Alonso, F., de los Reyes-
Guzm
´
an, A., Aznar, M. A., Alcubilla, C., P
´
erez-
Nombela, S., del Ama-Espinosa, A., Polonio-L
´
opez,
B., and Gil-Agudo,
´
A. (2016). Upper limb rehabili-
tation after spinal cord injury: a treatment based on
a data glove and an immersive virtual reality environ-
ment. Disability and Rehabilitation: Assistive Tech-
nology, 11(6):462–467.
Edmund Jacobson (1932). Electrophysiology of Mental
Activities. The American Journal of Psychology,
44(4):677–694.
Mizuguchi, N., Nakata, H., Uchida, Y., and Kanosue,
K. (2012). Motor imagery and sport performance.
The Journal of Physical Fitness and Sports Medicine,
1(1):103–111.
Montgomery, P. and Connolly, B. (2003). Clinical Applica-
tions for Motor Control.
Varoto, R. and Cliquet, A. (2015). Experiencing Func-
tional Electrical Stimulation Roots on Education, and
Clinical Developments in Paraplegia and Tetraplegia
With Technological Innovation. Artificial Organs,
39(10):E187–E201.
Villiger, M., Grabher, P., Hepp-Reymond, M. C., Kiper, D.,
Curt, A., Bolliger, M., Hotz-Boendermaker, S., Kol-
lias, S., Eng, K., and Freund, P. (2015). Relationship
between structural brainstem and brain plasticity and
lower-limb training in spinal cord injury: A longitu-
dinal pilot study. Frontiers in Human Neuroscience,
9.
BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices
226