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Upper Extremity Rehabilitation Robots: A Survey 327

extremity rehabilitation. “Handreha” is a hand-wrist haptic device that is
used for hemiplegic children rehabilitation (Bouri et al., 2013). Coaching
devices coach the individual by providing real-world practice via visual or
auditory feedback. For example, “T-WREX” monitors functional arm
movements during a home-therapy (Sanchez et al., 2004), and “DIEGO”
(from Tyromotion GmbH) with active gravity compensation and
“Microsoft Kinect” are used in virtual rehabilitation (Tseng et al., 2014).
Once again, selection of a suitable form of rehabilitation depends on the
patient’s condition and his/her level of disability. Recommending a general
guideline for this selection requires significant years of experience with
movement disorder therapy. Studies have shown that assisted therapy with
active devices is prevalant for most rehabilitation procedures, and other
forms of rehabilitation can be achieved by means of these active devices if
needed (Maciejasz et al., 2014).

5 CLASSIFICATION BY CONTROL SCENARIOS
Human arm motions are controlled by the biological feed-forward
and feedback control commands of the central nervous system (CNS)
(Mehrabi et al., 2017). The feed-forward commands are predicted using
an internal model of the arm. Feedback commands are corrective commands
generated by the assessment of movements by sensory organs. Any elec-
tronic controller that can maintain these characteristics might be advanta-
geous in rehabilitation robotics.
For exerting therapy approaches by upper extremity rehabilitation
robots, different control algorithms are utilized. The control inputs are
dynamic measurements such as force and torque signals, kinematic displace-
ment and velocity signals, and triggers such as switches and EMG signals.
Their feedbacks to the user are tactile, visual, auditory, or electrical
(FES). The control strategies for these robots are categorized as
(Maciejasz et al., 2014; Proietti et al., 2016) high- and low-level control sce-
narios. High-level control scenarios help to stimulate motor plasticity, and
low-level control scenarios are used to implement high-level scenarios.
These control scenarios with their subcategories are summarized in Fig. 2.

5.1 High-Level Control Scenarios
As shown in Fig. 2, there are three high-level control scenarios (Marchal-
Crespo and Reinkensmeyer, 2009; Maciejasz et al., 2014; Proietti et al.,
2016), which are assistive, resistive, and corrective control.