Syrebo SY-HR10A

The SY-HR10A is built for rehabilitation, neurology, geriatrics, paediatrics and orthopaedics departments. A therapist sets the exercise from the host and can change it without stopping the patient.

The manufacturer lists hand dysfunction caused by diseases or injuries, including stroke, traumatic brain injury, spinal cord injury, Parkinson's disease, hand injury, lymphedema and Alzheimer's disease. Paediatric cerebral palsy is also named. Because the modes cover every stage of rehabilitation, one robot serves a patient from first movement to refined finger control.

At the earliest stage, passive and cyclic training let the glove carry the hand through flexion and extension. Mirror and bilateral training bring the healthy hand into the exercise, with visual and proprioceptive feedback. As control returns, assistance and intention-driven training add force when the sensors detect movement signals, and resistance and active-resistive training give the hand a load to work against.

Task-oriented training uses daily hand tasks such as grasping a ball. Active game, active-virtual and active-grasping modes pair games with grip control, attention, calculation and cognition in daily living. A grip dynamometer measures the force the patient applies during the games, and the results feed into the evaluation.

Refined training targets single-finger flexion and extension and finger-to-finger pinch, while random gesture mode presents finger gestures of varying difficulty. Strength and coordination evaluations feed data-based reports. Multiple user profiles keep each patient's results, and reports can be printed for the file. The multilingual interface supports mixed-language teams, and the television connection lets patients follow the training screen from the table.

Parameters change mid-exercise

A nine-grade divided bar on the training screen lets a value change while the patient works. The exercise does not need to stop.

A simplified connection

The glove socket and the grip dynamometer socket sit at the front of the host. Cables reach the unit from the side the therapist faces.

Modes for every stage

Passive, assistance, active and resistance training cover the stages of rehabilitation. One robot follows the patient through them.

The healthy hand joins in.

Mirror and bilateral training use the healthy hand to guide the affected hand. The patient receives visual and proprioceptive feedback.

Force is added when movement starts.

In intention-driven mode, sensors detect subtle movement signals. The glove adds force to help complete the grip.

Games with a purpose

Active game, active-virtual and active-grasping modes give each exercise a goal. Grip control, attention and calculation are trained together.

Fine work on single fingers

Refined training covers single-finger flexion and extension, as well as finger-to-finger pinch. Random gesture mode adds gestures of varying difficulty.

Reports from each session

Strength and coordination evaluations produce data-based reports. The reports can be printed.

Profiles for every patient

Multiple user profiles store training data. A therapist returns to a patient's settings at the next session.

Pneumatic soft glove

Air pressure drives the glove through flexion and extension. The glove connects to the front of the host.

Nine-grade parameter adjustment

Most parameters are set in nine grades. A divided bar shows the grade on the training screen.

Passive and mirror training

The glove carries the hand through the movement, and mirror training brings the healthy hand into the exercise.

Assistance and resistance training

The glove supports the patient's movement or applies a load against it. The therapist picks the mode for the patient's stage.

Active game training

Games respond to the patient's effort. Each game sets an exercise goal.

Task-oriented training

Exercises copy daily hand tasks such as grasping a ball. Everyday movements become the practice.

Single finger training

Single finger exercises train one finger at a time. The therapist uses them for fine control.

Cyclic mode

The glove drives the affected hand through repeated flexion and extension. The patient relaxes while the hand moves.

Intention-driven mode

Sensors detect subtle movement signals, and the glove adds force to complete a grip. The patient starts the movement.

Bilateral mode

The healthy hand guides the affected hand through a grasp. Visual and proprioceptive feedback accompany the movement.

Active-resistive mode

The glove applies opposing force during flexion and extension. Strength work follows the same movement.

Active-virtual and active-grasping modes

Games train cognition in daily living, grip control, attention and calculation. A grip dynamometer works with interactive games to build grip strength.

Random gesture and refined training

Finger gestures of varying difficulty appear on screen. Refined training covers single finger movement and finger-to-finger pinch.

Strength and coordination evaluation

The system evaluates strength and coordination and builds data-based reports. Reports can be printed.

Multiple user profiles

Each profile stores a patient's training data. Training programmes can be personalised.

Television connection and multilingual interface

The unit connects to a television for the training screen. The interface serves therapists and patients in several languages.

The SY-HR10A suits a department that wants a therapist to tune each exercise as the patient works. The nine-grade adjustment on the training screen removes the pause between settings, and the front-mounted glove and grip dynamometer sockets keep connections simple.

A broad set of modes, from passive and mirror training to intention-driven, active-resistive and game training, lets one robot follow a patient through every stage. Multiple user profiles and printable reports keep each patient's results in one place, which suits rehabilitation, neurology, geriatrics, paediatrics and orthopaedics teams that treat hand dysfunction every day. The same host serves every patient in turn, because each user profile stores its own training data.