Shams Khan is a BC-based research engineer specializing in embedded systems and AI for wearable health technology. At Thompson Rivers University, he has delivered end-to-end systems that combine C/C++/Python firmware, BLE/MQTT data pipelines, and Raspberry Pi–based hardware with multi-sensor inputs (heart rate, inertial/gyro, and video). He led Well Care/WheelAssist, a wheelchair-mounted monitoring and safety platform with sub-second streaming and validated alert algorithms, and authored RHMS, a newly submitted journal paper proposing a scalable, secure, and device-agnostic framework for remote patient monitoring. His work includes hardware integration (custom enclosures, power management, sensor interfacing), backend/APIs and dashboards (Django), and experiment design for algorithm validation across variable breathing rates, motion, and environments. Shams has multiple peer-reviewed publications and received Best Paper at the International Conference on Advancement in Healthcare Technology & Biomedical Engineering. Beyond research, he has taught coding and robotics to K–12 students and contributes to startups that translate assistive-tech prototypes into deployable products. His focus: turning complex physiology and sensor data into dependable, field-ready experiences for athletes, patients, and clinicians.