Passive Haptic Learning (PHL) describes the learning of a motion, sequence or pattern without voluntary involvement of attention, focus or motivation through a haptic interface. In previous PHL studies about teaching Morse code, we suspect that active learning processes were at least partially involved in causing extraordinarily high learning rates. Therefore we conduct a similar, 50-participant user study to investigate whether PHL is able to teach Morse code without active attention. Our study design differs from previous studies by preventing active learning explicitly (no feedback given) and implicitly (non-Morse patterns, tests prevent learning by rule of elimination) for three groups. Two other groups get the same feedback as was present in other studies. Our results show much lower learning rates when there is no active learning possible. Including active learning, we replicate the same learning results as have been reported. This suggests that our suspicion was correct and that PHL of Morse Code is possible, but greatly limited. Further studies about PHL must pay attention to whether their study design allows participants to learn actively - even small amounts of information that is actively learned may have a great impact on learning performance.

Human Activity Recognition (HAR) with body-worn sensors has been studied intensively in the past decade. Existing approaches typically rely on data from inertial sensors. This paper explores the potential of using point cloud data gathered from wearable depth cameras for on-body activity recognition. We discuss effects of different granularity in the depth information and compare their performance to inertial sensor based HAR. We evaluated our approach with a total of sixteen participants performing nine distinct activity classes in three home environments. 10-fold cross-validation results of KNN and Random Forests classification exhibit a significant increase in F-score from inertial data to depth information (by > 12 percentage points) and show a further improvement when combining low-resolution depth matrices and sensor data. We discuss the performance of the different sensor types for different contexts and show that overall, depth sensors prove to be suitable for HAR.

Wearables like smartwatches or fitness trackers are increasingly entering everyday life. This offers new and unexplored opportunities to use their unique features - like the ability to act as vibrotactile actuators - as interfaces in computer games to increase game immersion. Current tactile cues in games are mostly restricted to event based-feedback and practically exclusive to console gaming. This paper presents LifeTact, a system which informs players about remaining hit points through a tactile heartbeat. The Tactile heartbeat is emitted via existing wearables like smartwatches or fitness trackers. The system was implemented and evaluated in a between-subject study with gamers, who rated the system as innovative, beneficial to the user experience and immersive.