The aim of this experiment is to collect preliminary data to guide future studies to assess whether training based on serious gaming and conventional methods lead to different functional outcomes for DC and ML control. Moreover, the aim is to find…
ID
Source
Brief title
Condition
- Other condition
Synonym
Health condition
amputaties dan wel aangeboren afwijkingen aan de bovenste ledematen
Research involving
Sponsors and support
Intervention
Outcome measures
Primary outcome
Outcome scores from a battery of clinical tests measuring prosthetic control
capability and functional outcome. The primary outcome variable is the Partial
Southampton Hand Assessment Procedure (P-SHAP) score (SHAP is a functional test
often used in prosthetic research).
Secondary outcome
Secondary outcome variables are: Score in Clothespin Relocation Test, Score on
Hysteresis test, Joint angles ranges and time to completion in tasks which
resemble activities of daily living (ADL), and the System User Scale score.
Other measurements are: Accuracy scores and games scores during training
sessions.
Background summary
In the field of myoelectric upper limb prostheses, a huge gap exists between
the mechanical/electrical functions of prosthetic hands and the possibilities
of the user to control those (i.e. the human-machine interface). Traditional
control (called *direct control*, DC) has shown to be highly non-intuitive,
resulting in device abandonment rates in the range of up to 28%, even with
state-of-the-art prosthetic hands. To overcome the non-intuitiveness of DC, a
form of control based on machine learning algorithms (ML) has been developed.
However, until now it remains unproven whether ML control is superior to DC.
Moreover, no well-grounded training scheme exists for neither DC nor for ML
control, even though it is widely known that quality of training highly affects
the satisfaction with prostheses and thus the risk of device abandonment.
Serious games (e.g. video games that are fun/engaging to play and at the same
time teach the user specific skills), have often been suggested in the
literature for myoelectric training with DC and ML control, but controlled
studies with patients are lacking.
Study objective
The aim of this experiment is to collect preliminary data to guide future
studies to assess whether training based on serious gaming and conventional
methods lead to different functional outcomes for DC and ML control. Moreover,
the aim is to find out whether ML control and DC control lead to different
functional outcomes.
Study design
Explorative intervention study with pre-posttest design.
Intervention
Four groups will undergo a different training paradigm: Two groups will undergo
standard clinical training for DC and ML, respectively. The other two groups
will undergo training based on a serious game for DC and ML, respectively. A
fifth group will establish a baseline and will not undergo any interventions.
Study burden and risks
All participants (except for the reference group) will start with a fitting
session. During this session all participants will be fitted with a plaster
custom-made socket. After the socket is fitted a 20 minute introductory session
with the control mechanism will take place. In the next session the pre-test
will be conducted. This session will last around 120 minutes of which 60
minutes consists of resting periods to not fatigue the muscles producing the
control signals. In the seven sessions after the pre-test participants will
partake in 7 training sessions of 45 minutes where they will use the muscles in
the stump to practice myoelectric control. After these training sessions one
last session containing the post-test will take place. This day is similar to
the pre-test. The pre- and post-test will be held on days separate from any
training session.
The risk associated with participating in this study are negligible, with
muscle fatigue as the only risk. The burden is minimal since all sessions will
take place under supervision within a month at a place chosen by the
participant such as the participants home or at a clinic.
Hanzeplein 1
Groningen 9713 GZ
NL
Hanzeplein 1
Groningen 9713 GZ
NL
Listed location countries
Age
Inclusion criteria
Inclusion criteria are:
- 18 years of age or older
- Upper limb deficiency at transradial or wrist level
- Unilateral limb deficiency
- Experience with myo-electric prosthesis
- No experience with more than one of the control types that will be examined
- Written informed consent
- Mastering the Dutch language
Exclusion criteria
Individuals that are younger than 18 years of age and/or have experience with both machine learning and direct control will be excluded. Furthermore, individuals with an upper limb deficiency at another level than transradial or wrist level will not be included. In addition individuals with an amputation or congenital defects of both hands will be excluded.
Design
Recruitment
Medical products/devices used
Followed up by the following (possibly more current) registration
No registrations found.
Other (possibly less up-to-date) registrations in this register
No registrations found.
In other registers
Register | ID |
---|---|
CCMO | NL65604.042.18 |
Other | UMCG Research register: 201800308, NTR7155 |