SBIR Phase I: Artificial Intelligence (AI)-Powered, Wearable Technology to Monitor and Assess Strength Training Activities

SBIR 第一阶段：人工智能 (AI) 支持的可穿戴技术，用于监控和评估力量训练活动

• 批准号: 2227835
• 负责人: Dmitry Popov
• 金额: $ 27.5万
• 依托单位: WURQ, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227835&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; Intelligence; AI

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in developing improved assessment tools and as a result, improved knowledge about muscle health and strength training. The Center for Disease Control (CDC) data shows that 76 million people in the US do strength training consistently and meet exercise requirement standards. The social and health benefits of strength training like muscular growth, fat reduction, better body balance, and improved mental health have been well characterized in numerous peer reviewed studies. There are many wearable devices on the market that can monitor cardiovascular health via heart and respiratory rates, but muscle health data is missing, because monitoring strength training is much more difficult. This project will develop a low-profile, artificial intelligence (AI)-driven wearable system composed of wrist and torso sensors. The proposed system will automatically recognize a training program, learn about the user goals and training experience, and provide step by step guidance, individualized to the user's abilities and activities. The solution will work in any environment (e.g., home, gym, or outdoor location). The system will be able to initiate, maintain, and improve strength training health regimens.This Small Business Innovation Research Phase I project will combine AI and signal processing techniques to enable inertial measurement unit (IMU)-based sensors to robustly detect strength training movements, accurately count repetitions, and provide performance metrics like time under tension, exerciser pace, amount of the total work during an exercise, power generated, range of motion, etc. Towards this end, an optimized deep-learning model will be built that will detect 15 strength training exercises with 99% accuracy and will miss no more than one out of hundred repetitions. Another key objective is the creation of a neural network structure to reduce IMU drifts via feature aggregation of the knowledge of an exercise type being performed and human kinematics. The anticipated technical result is performance assessment with a root-mean-square deviation of less than 0.02 m for trajectory and less than 0.025 m/s for velocity. Finally, anticipated results include documentation of user needs aligned with implemented and future product features.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响 /商业潜力在于开发改进的评估工具，因此，对肌肉健康和力量训练的知识得到了改善。 疾病控制中心（CDC）的数据表明，美国有7600万人始终如一地进行力量训练并符合运动要求标准。 力量训练的社会和健康益处，例如肌肉生长，减少脂肪，更好的身体平衡和改善的心理健康，在许多同行评审研究中都得到了很好的特征。市场上有许多可穿戴设备可以通过心脏和呼吸率监测心血管健康，但是肌肉健康数据缺少，因为监测力量训练要困难得多。该项目将开发由腕部和躯干传感器组成的低调的人工智能（AI）驱动的可穿戴系统。 拟议的系统将自动识别培训计划，了解用户目标和培训经验，并逐步提供指导，并将其个性化符合用户的能力和活动。该解决方案将在任何环境（例如家庭，健身房或室外位置）中使用。 The system will be able to initiate, maintain, and improve strength training health regimens.This Small Business Innovation Research Phase I project will combine AI and signal processing techniques to enable inertial measurement unit (IMU)-based sensors to robustly detect strength training movements, accurately count repetitions, and provide performance metrics like time under tension, exerciser pace, amount of the total work during an exercise, power generated, range of motion, etc. Towards这将建立一个优化的深度学习模型，该模型将以99％的精度检测15次力量训练练习，并且将不超过一百次重复。另一个关键目标是创建神经网络结构，以减少IMU漂移，这是通过对正在执行的运动类型和人类运动学的知识的特征聚集。预期的技术结果是绩效评估，轨迹的根平方偏差小于0.02 m，速度小于0.025 m/s。最后，预期的结果包括与实施和未来产品功能保持一致的用户需求的文档。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。