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According to theories of Embodied Cognition, memory for words is related to sensorimotor experiences collected during learning. At a neural level, words encoded with self-performed gestures are represented in distributed sensorimotor networks that resonate during word recognition. Here, we ask whether muscles involved in gesture execution also resonate during word recognition. Native German speakers encoded words by reading them (baseline condition) or by reading them in tandem with picture observation, gesture observation, or gesture observation and execution. Surface electromyogram (EMG) activity from both arms was recorded during the word recognition task and responses were detected using eye-tracking. The recognition of words encoded with self-performed gestures coincided with an increase in arm muscle EMG activity compared to the recognition of words learned under other conditions. This finding suggests that sensorimotor networks resonate into the periphery and provides new evidence for a strongly embodied view of recognition memory.

Be-In/Be-Out (BIBO) is considered a simple paradigm for hands-free interaction in the domain of multimodal public transport systems where travellers automatically obtain their tickets by entering and leaving means of transportation. The infrastructure in the vehicles detects the passengers' presence and initiates services unnoticed in the background. Whereas first implementations of BIBO systems are based on active Radio Frequency Identification (RFID), where users carry small RFIDtags in their pockets, we focus on Bluetooth Low Energy (BLE) as an alternative but potentially more powerful technology for this domain. We try to carve out differences, accentuate arising opportunities for interaction and evidence the applicability of BLE for BIBO systems by a series of conducted screening tests.

Intelligent tutor systems (ITSs) in mobile devices take us through learning tasks and make learning ubiquitous, autonomous, and at low cost (Nye, 2015). In this paper, we describe guided embodiment as an ITS essential feature for second language learning (L2) and aphasia rehabilitation (ARe) that enhances efficiency in the learning process. In embodiment, cognitive processes, here specifically language (re)learning are grounded in actions and gestures (Pecher and Zwaan, 2005; Fischer and Zwaan, 2008; Dijkstra and Post, 2015). In order to guide users through embodiment, ITSs must track action and gesture, and give corrective feed-back to achieve the users' goals. Therefore, sensor systems are essential to guided embodiment. In the next sections, we describe sensor systems that can be implemented in ITS for guided embodiment.

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