Event:

Regaining independence in daily life is central to stroke rehabilitation, yet clinical assessments capture only what patients can do in structured settings, not what they actually do in real-world contexts. In fact, many stroke survivors regain the capacity to perform tasks with their affected arm during therapy but rarely use it in daily life; a discrepancy known as the capacity–performance gap. To better understand this gap, arm activity performance must be quantified objectively and continuously using inertial measurement units (IMUs). Yet, current IMU-based algorithms often fail to detect functional movements, those purposeful, goal-directed actions that matter for independence and recovery.
This talk will outline the challenges of functional movement detection and present optimized, personalized threshold-based algorithms that enhance IMU-based arm-use estimation. Applied in a longitudinal stroke cohort, these methods reveal that arm activity performance plateaus earlier than motor capacity, underscoring the need to monitor both dimensions of recovery.

By the end of this session, you will:
Understand how IMU data are processed to derive arm-use metrics.
Appreciate why functional arm movement detection algorithms require optimization and personalization.
Recognize what true functional recovery looks like when we measure what truly matters.