Human Universal Remote | SBIR

OUSW (R&E) CRITICAL TECHNOLOGY AREA(S): Applied Super Intelligence

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

PROJECTED CMMC LEVEL REQUIREMENT: Level 2 (self)

Objective: The objective of the Human Universal Remote DP2 SBIR topic is to further develop surface electromyography (sEMG) sensors, piezoelectric micromachined ultrasonic transducers (PMUTs), or other non-invasive sensor modalities as a user interface for warfighter multi-system operations1-6. The cited references serve as representative baselines of the state-of-the-art human machine interfaces and do not limit the scope of acceptable technical approaches.

The vision is for a single warfighter to execute simultaneous, multi-domain tasks without compromising their primary physical posture or situational awareness. For example, controlling a drone while handling a rifle, or sending/receiving audible, out of line-of-sight communications via radio without speaking. The envisioned system may combine micro-scale ultrasonic cellular stimulation, ultrasound and EMG sensor fusion, and deep learning driven gesture recognition. The system must translate and discern between sub-millimeter muscle contractions and intentional, full-range movements to trigger external system commands. In a series of readiness tests, performers will develop the sensor suite and processing framework to serve as a hands-free, eyes-free human-machine interface for multi-system warfighter operations.

Description: Surface electromyography (sEMG) is a proven technique for detecting biomedical voltage measurements from the skin generated by the electrical current of muscle fibers during skeletal muscle contraction and relaxation. These signals, which reflect neuromuscular activity, typically range in amplitude from 0 to 10 millivolts and operate at frequencies between 20 and 450 Hz. While historically used to restore hand function for transradial amputees via non-invasive fiber membrane potential measurement, this topic proposes leveraging state-of-the-art sEMG in concert with other non-invasive muscle sensing modalities to significantly enhance the operational capability of the warfighter. For example, ultrasound is a high-resolution non-invasive sensing modality capable of probing deep muscle tissues and providing complementary information to electromyographic signals. This topic is divided into two distinct, parallel Technical Areas (TAs) that must be addressed concurrently. Proposers must deliver an integrated solution spanning both domains:

Technical Area 1 (TA1): Microphone
Signal capture will focus on the non-invasive detection and extraction of EMG signals and/or electrical, acoustic deep muscle sensing signals from the user. The approach should include the utilization of signals for arm control as both a command and as biological sensors to understand where muscle activity is occurring and to function as an active indicator of warfighter stress. Hardware systems developed under this Technical Area must reliably detect, isolate, and extract these microvolt-to-millivolt level biopotential signals directly from the warfighter’s skin without invasive techniques. Proposers should address the following TA1 criteria:

Technical Area 2 (TA2): Speaker
This Technical Area focuses on translating decoded physiological intent into standardized, platform-agnostic output modalities. The system must reliably differentiate between passive muscle strain and active, intentional micro- and macro-gestures to generate high-confidence external commands. The approach should include command output that translates the processed signals into an actionable trigger via standard communications channels designed for easy integration into Team Awareness Kit (TAK). To prevent over-engineering to a singular use case or platform, the generated signaling protocol should be standardizable for use across multiple external systems. At a minimum, the platform should be built with at least one communications channel using radiofrequency (RF). Example platforms include enabling users to task robotic systems, low-level ISR drones, comms drones, trigger communications, and generate visual indicators directly on a TAK display. Proposers should address the following TA2 criteria:

Phase I

Since this is a DP2 topic ONLY, a successful proposal must already contain evidence of a working prototype. The success criteria for a working prototype are the following:

• Working non-invasive sEMG sensor array that employs a computationally efficient machine-learning based control algorithm driven by input from a high-density array of electrodes.
• Functioning hardware that interfaces with a machine learning algorithm capable of real-time gesture classification.
• A developed training and calibration software interface that prompts users through gesture protocols, records performance metrics, measures gesture classification accuracy, and misclassification error rates.

The above information should be presented in such a manner that it is evident that the proposer’s prototype technology can be tailored to meet the criteria in Phase II.

Phase II

A successful proposal will address the overarching technical areas with performer-defined metrics over the course of 18 months or less. The program is structured with readiness tests at Month 12 and Month 18. Proposers will be expected to define at least one deliverable per month to meet incremental technical milestones to sufficiently advance the technology to meet the Controlled Environment Readiness and Environmental Resilience tests. Proposals should outline a work plan to technically advance the design to meet the demonstration scenarios within the provided timeline.

Controlled Environment Readiness Test – Jam Session

This readiness test will serve as the gatekeeper metric to prove the system can successfully transition from controlled development environments into unpredictable operational settings. The system must be tested on a diverse cohort of previously untrained and unexposed human subjects. The software calibration must adapt to varied anatomical structures, body mass indexes, and physiological signal variations without developer intervention. Testing will occur during a series of continuous baseline of physical activities. These activities will include high-exertion tasks designed to introduce physical noise to test rejection of background muscle activity. While performing the baseline physical activities, subjects will be prompted to execute specific micro- and macro-command gestures. The system must dynamically isolate and decode these intentional commands from the background physiological "noise" of the active baseline tasks.

Environmental Resilience – Arena Concert

The Month 18 evaluation will transition the system from controlled laboratory settings into an austere field environment at a representative military training installation (e.g., Marine Corps Base Camp Pendleton), subjecting the user to the following parameters while executing gestures for one command output that translates the processed signals into an actionable trigger via RF and/or ATAK integrations:

• Ingress Protection (IP) rating IP68
• In tandem with standard military uniform/dress
• Chemical, biological, radiological, and nuclear (CBRN) protective gear
• High-exertion physical activity/collision testing
• Simulated weapon manipulation

Gesture Output can include any of the following, for example:

• Audible communication without speaking and out of line-of-sight
  • Ground and Water Testing
• Autonomous system tasking of any variety
• TAK manipulation
• Radio control
• Demonstration of ability to remove radio from weapons space

Phase III dual use applications

Phase III will be oriented toward military transition and further commercialization.

• Military: Autonomous system tasking, enabling raid teams to communicate without speaking, and providing visuals and status through fatal funnel.
• Commercial: Accessibility technology, quantitative biomechanical baselines to monitor neuromuscular health for rehabilitation, open-source code for personal drone piloting and gaming applications.

References

1. Balasubramanian, P. S., Singh, A., Xu, C., & Lal, A. (2020). GHz ultrasonic chip-scale device induces ion channel stimulation in human neural cells. Scientific Reports, 10(1), Article 3075.
2. Rabe, K. G., & Fey, N. P. (2022). Evaluating electromyography and sonomyography sensor fusion to estimate lower-limb kinematics using Gaussian process regression. Frontiers in Robotics and AI, 9, Article 716545.
3. Sharma, V., Lloyd, E., Faltys, M., Ortiz-Catalan, M., & Glass, C. (2025). Assessment of gesture accuracy for a multi-electrode EMG-sensor-array-based prosthesis control system. Prosthesis, 7(4), 99.
4. Scheme, E. J., Englehart, K. B., & Hudgins, B. S. (2011). Selective classification for improved robustness of myoelectric control under nonideal conditions. IEEE Transactions on Biomedical Engineering, 58(6), 1698–1705.
5. Shi, X., He, Z., Zheng, L., Chen, Y., Bai, W., Xu, M., & Zhu, R. (2026). MSCB-DualAttn Network for sEMG-Based Gesture Recognition in Transradial Amputees with Varying Residual Limb Lengths. Symmetry, 18(5), 869.
6. Yang, W., Tang, Z., Zhu, K., Zhou, G., & Xie, J. (2026). Wearable multimodal sensing using piezoelectric micromachined ultrasonic transducer and surface electromyography for hand gesture recognition. Measurement, 290, Article 110650.

Keywords

Human Universal Remote, Autonomous System Tasking, Wearables, EMG, Human/Machine Teaming, Human/Machine Interface, Biosignal Translation

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