Yes, according to a secondary report from August 2025. YouTuber Basically Homeless (Nicholas) reportedly built a prototype called “Neuromuscular Aim Assist” for Counter-Strike 2. A computer-vision system on a PC detects enemies on screen, and electrical stimulation applied to his arm reportedly moves the arm toward those targets. The creator’s own video and hardware list were not available for this review, so the details below are the report’s account rather than independently verified results.
What the prototype is made of
The report describes four main parts. Each is summarized below as the report presents it.
- Object detection on the PC. A YOLO (You Only Look Once) model was reportedly trained on Counter-Strike 2 gameplay to locate enemy positions in the game image.
- An electrical stimulation unit. The report calls it an EMS or TENS device. EMS (electrical muscle stimulation) and TENS (transcutaneous electrical nerve stimulation) are both devices that pass electrical pulses through the skin, but the report does not say which mode was used or at what settings.
- A Raspberry Pi controller. The PC sends commands to a Raspberry Pi, which reportedly switches relays.
- Relays and electrodes on the arm. The relays connect the stimulation unit to electrodes on the creator’s arm. A later iteration reportedly aimed to contract the trigger finger as well.
The report does not name exact models for the stimulation unit, relays, electrodes or computer-vision software, and it does not publish a parts list or wiring description.
How the control chain works
As described, the system runs in this order on each cycle:
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- The PC captures the game image while Counter-Strike 2 is running.
- The YOLO model finds enemy positions in that image.
- The PC sends movement commands to the Raspberry Pi over the network connection the report later credits with reducing latency.
- The Raspberry Pi switches relays on or off.
- The relays route stimulation to electrodes on the arm, and the resulting muscle contractions move the arm toward the detected target.
The main point of the design is that the software suggests where to aim, while the body is the actuator. That is why the report discusses the system in terms of “machine intervention” in the player’s movement rather than as a conventional aim script that only changes what appears on screen.
Reported performance: how the early versions compare with later changes
The report describes improvements between early and later versions but does not publish test data. The table reflects only what the report says.
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| Dimension | Early versions (as described) | Later iteration (as described) |
|---|---|---|
| Response time | Noticeable latency that made the arm lag behind intended movement | Reported as under 100 ms, associated with Ethernet, upgraded hardware, solid-state relays and more precise muscle targeting. No test method or raw measurements are given. |
| Detection errors | False detections sometimes twitched the aim toward teammates. Frequency not stated. | Not stated whether false detections were reduced. |
| Player control | The stimulation sometimes overrode the creator’s own reflexes. | Not stated whether the override was reduced; the report describes machine-driven movement as the core mechanism. |
| Physical comfort | Discomfort reported. | Not stated. The report says prolonged use was physically strenuous. |
The under-100 ms figure
The “under 100 ms” response time is the report’s claim, attributed to the prototype’s later improvements. It is not an independently measured benchmark, and the report does not say how the time was measured, over how many trials, or whether it covers the full path from image capture to muscle movement. Treat it as a claim about one prototype rather than a general performance result.
Reaction-time comparisons
The report also cites an average human reaction time of about 200 milliseconds and says competitive esports players can reach “the 150ms mark, or even below.” Neither figure is linked to an original study in the report, so they should not be treated as established statistics. They are useful only as the framing the report uses to explain why 100 ms matters.
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Did it count as cheating?
The report says teammates did not consider the system cheating. The creator reportedly described the situation as a “gray area.” That wording is quoted from the report’s account, and the report does not give a formal role or direct link to the original video for it.
No official ruling from the game’s developer, a tournament organizer or a platform is cited in the report. Whether the system would be allowed in any competition is therefore not established by this coverage.
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Physical strain, safety and why not to copy it
The report describes three practical problems: false detections, discomfort in early versions, and physical strain during longer sessions. It does not document safety testing, clinical review or guidance on electrical stimulation of the arm, and this article does not supply any.
Electrical stimulation delivered through electrodes on the body can cause pain, skin irritation and muscle injury, and it is not safe for everyone. People with heart devices, pacemakers, epilepsy or pregnancy, among other conditions, should not use stimulation devices without medical advice. Anyone interested in the concept should treat the prototype as a demonstration described in one report, not as a setup to reproduce.
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What is and is not established
- Established by the report: the project exists as described, uses computer vision for Counter-Strike 2 detection, sends commands to a Raspberry Pi that drives relays, and moves the arm through electrical stimulation.
- Reported but not verified: the under-100 ms response time, the reasons for each improvement, and the claims about teammate reactions.
- Not covered by the report: the exact hardware, the creator’s video, independent testing, the project’s current status, and any official policy on the system.
The report is dated 14 August 2025. This article does not track whether the prototype has changed since then.
The coverage traces back to an iQOO Community repost, which credits Game Rant, under the title “YouTuber Builds Aimbot That Controls Their Body While Gaming.”
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