IEEE Robotics & Automation Magazine - December 2017 - 105
enough to capture hand motion. System structures for offline
and online data acquisitions and analysis are shown in Figures 2 and 3, respectively.
Training Procedure for Real-Time System Use
To use the system online, the subject was required to record
at least one repetition of the multiple different grips he wanted to accomplish for training purposes. Then, with the provided interface, the user could build the predictive model
and start using the bebionic 3 hand. The minimum number
of grips was three, and the maximum was set to 14 (the total
number of configurable grips provided by the bebionic 3).
The labeling of the grips was hard-coded into the program,
and these labels were then used to send proper control signals to the robotic hand. The first three grips were relax,
Cypress Chips
Start
Send/Receive
Digital Commands to/from UI
open, and closed fist (force), which simulated the conventional on-off control.
Experiment Setup and Protocol
All of the experiments carried out for this study were
approved by the Simon Fraser University Office of Research
Ethics in Burnaby, British Columbia, Canada. Although several studies have been done to investigate various HMIs for
static control of prosthetic hands, dynamic motions are better representatives of a real-case scenario of prosthesis usage.
Because this study's objective was to investigate a practical
system, all experimental data were recorded using the
dynamic protocol [11] shown in Figure 4. The dynamic protocol required the participant to move his or her arm in a circular motion through the humeral plane to cover the area
BBB
Receive
Data from Cypress Chips
Save Data on BBB Memory
(b)
Acquire Data
Offline Processing
Build Machine-Learning Model
Predict User Intentions Based on
Sensory Data and the Machine-Learning Model
(a)
(c)
Figure 2. The software architecture for experimental data collection and offline processing. (a) The sensory data collection is started
by the user interface (UI) and performed by Cypress chips. (b) The BBB receives and saves sensory data. (c) Offline processing uses
recorded data to obtain results.
BBB
Cypress Chips
Acquire Data
Start
Send/Receive
Digital Commands to/from UI
Send Control
Command to the Hand
Receive Data from Cypress Chips
Build Machine-Learning Model
Predict User Intentions Based on
Sensory Data and the Machine-Learning Model
(a)
(b)
Figure 3. The software architecture for online use of the device. (a) Cypress chips interact with the UI, acquire sensory data, and send
control signals to the bebionic 3 hand when commanded. (b) The BBB receives data, builds a machine-learning model, and executes
real-time prediction.
DECEMBER 2017
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IEEE ROBOTICS & AUTOMATION MAGAZINE
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105
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