Atrioventricular Synchrony Delivered by a Dual-Chamber Leadless Pacemaker System.

BACKGROUND: A dual-chamber leadless pacemaker system has been designed for AV synchronous pacing using wireless, beat-to-beat, implant-to-implant (i2i) communication between distinct atrial and ventricular leadless pacemakers. The AV synchrony achieved across various ambulatory scenarios has yet to be systematically evaluated. METHODS: A prospective, single-arm, unblinded, multicenter, international clinical trial of the leadless pacemaker system was conducted in patients with a conventional dual-chamber pacing indication enrolled from February 2022 to March 2023. Leadless pacemaker systems were implanted, and 12-lead Holter electrocardiographic recordings were collected 3 months after implantation over various postures/activities: sitting, supine, left lateral recumbent, right lateral recumbent, standing, normal walk, and fast walk. An independent Holter core laboratory performed a manual adjudication of the percent of AV synchronous beats using the standard 300-millisecond PR interval limit. Atrium-to-ventricle and ventricle-to-atrium i2i communication success rates were also assessed. Post hoc summary statistics describing the relationships between AV synchrony and i2i success, posture/activity, implantation indication, AV event, and heart rate were calculated. RESULTS: In the evaluable population (n=384 of 464 enrolled [83%]; 61% male; age, 70 years; weight, 82 kg; 60% ejection fraction; 95% of beats evaluable), the mean AV synchrony of 98% of beats observed across all postures using the standard 300-millisecond limit was greater than both atrium-to-ventricle i2i (94%) and ventricle-to-atrium i2i (94%; P<0.001), exceeding both i2i values in 95% of patients. AV synchrony was achieved in >95% of evaluable beats across all postures/activities, implantation indications, AV paced/sensed event combinations, and heart rate ranges (including >100 bpm). CONCLUSIONS: This dual-chamber leadless pacemaker system demonstrated AV synchrony in 98% of evaluable beats at 3 months after implantation. AV synchrony was maintained across postures/activities and remained robust for heart rates >100 bpm.

Using machine learning to blend human and robot controls for assisted wheelchair navigation.

This work presents an algorithm for collaborative control of an assistive semi-autonomous wheelchair. Our approach is based on a statistical machine learning technique to learn task variability from demonstration examples. The algorithm has been developed in the context of shared-control powered wheelchairs that provide assistance to individuals with impairments that affect their control in challenging driving scenarios, like doorway navigation. We validate our algorithm within a simulation environment, and find that with relatively few demonstrations, our approach allows for safe traversal of the doorway while maintaining a high level of user control.