{"id":216462,"date":"2026-05-04T11:44:10","date_gmt":"2026-05-04T15:44:10","guid":{"rendered":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/?p=216462"},"modified":"2026-05-04T11:44:10","modified_gmt":"2026-05-04T15:44:10","slug":"sensorimotor-integration-system-for-enhancing-neuromuscular-recovery-in-post-surgical-athletes","status":"publish","type":"post","link":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/sensorimotor-integration-system-for-enhancing-neuromuscular-recovery-in-post-surgical-athletes\/","title":{"rendered":"Sensorimotor Integration System for Enhancing Neuromuscular Recovery in Post-Surgical Athletes"},"content":{"rendered":"\n
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Group Members:<\/strong> Shahad Alhassani, Yiduo Liu, Riley Lopus, Mia Strassman, and Jessie Talasco<\/p>\n\n\n\n

Supervisor: <\/strong>Dr. Michael Giacomelli<\/p>\n\n\n\n

Customer: <\/strong>Dr. Ram Haddas, URMC Motion Laboratories<\/p>\n<\/div>\n<\/div>\n\n\n\n

Problem Statement:<\/strong> Motion analysis laboratories currently lack a reliable, low-latency, adaptable method to trigger unanticipated, reactive movements synchronized with biomechanical data collection during rehabilitation tasks. This limits assessment of reactive neuromuscular control and return-to-play decisions for post-surgical athletes. We aim to build a cueing system that detects movement initiation in real time and delivers unpredictable cues to force reactive movement, enabling a more “real world” valid evaluation of neuromuscular control during rehabilitation.<\/p>\n\n\n\n

Customer Scenario: <\/strong><\/p>\n\n\n\n

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  • URMC Motion Analysis Laboratories\n
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    • Team of engineers, technicians, physical therapists and doctors<\/li>\n\n\n\n
    • Engineers and technicians will be the ones using our device<\/li>\n\n\n\n
    • Physical therapists will be observing the athlete<\/li>\n<\/ul>\n<\/li>\n\n\n\n
    • Return to sport protocol\n
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      • Box drop jump test<\/li>\n\n\n\n
      • Eliminate learnability with random cues<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
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        Design Overview:<\/strong><\/p>\n\n\n\n

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        • Internal Sensor\n
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          • Headband with 4 reflective markers<\/li>\n\n\n\n
          • Marker data -> IR camera -> Vicon<\/li>\n\n\n\n
          • Motion detection via MATLAB<\/li>\n<\/ul>\n<\/li>\n\n\n\n
          • External Sensor\n
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            • IR breakbeam sensor<\/li>\n\n\n\n
            • 0V when blocked, 5V when light passes<\/li>\n\n\n\n
            • Rising edge detection via MATLAB<\/li>\n<\/ul>\n<\/li>\n\n\n\n
            • GUI\n
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              • Choose test type, cue type (visual\/audio\/mixed), sensor (headband\/photogate\/both), and number of repeats per cue type<\/li>\n<\/ul>\n<\/li>\n\n\n\n
              • Cue type and timing (in Vicon Frames) saved to CSV for post processing.<\/li>\n<\/ul>\n<\/div>\n\n\n\n
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