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The COMPLETE CONTROL System Gen2 is to be used exclusively for external prosthetic fittings of the upper limbs.

The COMPLETE CONTROL System Gen2 is an advanced control solution designed to provide the functionality of a powered myoelectric prosthesis for upper extremity amputees. The COMPLETE CONTROL System Gen2 employs pattern recognition technology to non-invasively acquire user-specific muscle signals for the control of industry-standard upper extremity prostheses. Patients can achieve control of their devices, eliminate control switching, and benefit from quick and powerful recalibration. The COMPLETE CONTROL System Gen2 simplifies electrode placement and allows a prosthetist to spend less time adjusting system settings and configurations.

The COMPLETE CONTROL System Gen2 is designed to work seamlessly with most major manufacturers' devices as an easy plug-and-play add-on and does not require an additional battery.

The COMPLETE CONTROL System Gen2 is an embedded system that is used in conjunction with an upper-limb prosthetic device. The system has been validated for a specific set of prosthetic elbow, wrist and hand components which are listed in the user manual.

The COMPLETE CONTROL System Gen2 contains the following components:

• COMPLETE CONTROLLER main processor
• Device Interface Cable (clinician-specified termination type)
• EMG Interface Cable (clinician-specified termination type)
• COMPLETE CALIBRATE Button (part of COMPLETE CONTROLLER)
• Fabrication aid for the COMPLETE CONTROLLER
• Socket cut-out template for the COMPLETE CALIBRATE Button
• COMPELTE CONTROLROOM Application
• COMPLETE COMMUNICATOR Dongle

The acceptance criteria and study proving the device meets these criteria are detailed below. It is important to note that this document is a 510(k) summary for a medical device (COMPLETE CONTROL System Gen2), which aims to demonstrate substantial equivalence to a predicate device, not necessarily to independently prove the device's efficacy through extensive clinical trials as would be required for a novel device.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a distinct "test set" in terms of patient data or typical sample sizes seen in clinical trials. The performance evaluation primarily focuses on non-clinical bench testing and adherence to international electrical safety and EMC standards. Therefore, an explicit sample size for human subjects or their data provenance (country of origin, retrospective/prospective) is not applicable in the context of this 510(k) summary, as human clinical testing was not required for this submission. The tests performed were on the device itself.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not applicable as the evaluation primarily involved non-clinical bench testing and adherence to standards, not the establishment of ground truth for diagnostic or prognostic interpretations by clinical experts.

4. Adjudication Method for the Test Set

This information is not applicable for the same reasons as above. The testing was objective measurement against predefined technical specifications and international standards.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, an MRMC comparative effectiveness study was not conducted and is not mentioned in this 510(k) summary. The submission focuses on demonstrating substantial equivalence to a predicate device through technical and performance comparisons, not on measuring human reader improvement with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Study was Done

The testing described is primarily standalone in nature, as it evaluates the device's adherence to electrical safety standards, EMC, and its internal functionality (cabling, power, Bluetooth, inputs, outputs, calibration, pattern recognition, file save). The "algorithm only" aspect is embedded within the "Calibration and Pattern Recognition Test," which confirms the device's ability to perform its core function. However, this is integrated into the device's overall performance testing rather than a separate, isolated algorithm-only study as might be conducted for an image analysis AI.

7. The Type of Ground Truth Used

The "ground truth" for the non-clinical tests was adherence to established international standards (IEC 60601-1 for electrical safety and physical characteristics, IEC 60601-1-2 for EMC) and the device's own internal design specifications and requirements. For the functional tests, the "ground truth" was whether the device performed its intended function as designed (e.g., connected, powered on, saved files).

8. The Sample Size for the Training Set

The document does not provide information about a "training set" sample size. The device uses "pattern recognition technology to non-invasively acquire user-specific muscle signals." For such systems, the "training set" typically refers to individual patient-specific muscle signal data used to train the system for that particular user. This is a personalized calibration process, not a large, general training dataset used for machine learning model development in the traditional sense as this is a medical device, not a diagnostic AI.

9. How the Ground Truth for the Training Set Was Established

As noted above, for myoelectric pattern recognition systems, the "training" involves a user-specific calibration process. The document states, "Patients can achieve control of their devices, eliminate control switching, and benefit from quick and powerful recalibration." This implies that the "ground truth" for each user's calibration is derived from their own movements and muscle signals, allowing the system to learn the unique patterns associated with their intended prosthetic movements. The system does not rely on a pre-established "ground truth" from a large, independent dataset for its core pattern recognition function, but rather on real-time muscle signal acquisition and mapping by the individual user.

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