Despite the importance of touch in human–human relations, research in affective tactile practices is in its infancy, lacking in-depth understanding needed to inform the design of remote digital touch communication. This article reports two qualitative studies that explore tactile affective communication in specific social contexts, and the bi-directional creation, sending and interpretation of digital touch messages using a purpose-built research tool, the Tactile Emoticon. The system comprises a pair of remotely connected mitts, which enable users in different locations to communicate through tactile messages, by orchestrating duration and level of three haptic sensations: vibration, pressure and temperature. Qualitative analysis shows the nuanced ways in which 68 participants configured these elements to make meaning from touch messages they sent and received. It points to the affect and emotion of touch, its sensoriality and ambiguity, the significance of context, social norms and expectations of touch participants. Findings suggest key design considerations for digital touch communication, where the emphasis shifts from generating ‘recognizable touches’ to tools that allow people to shape their touches and establish common understanding about their meaning.

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Voice control has emerged as a popular method for interacting with smart-devices such as smartphones, smartwatches etc. Popular voice control applications like Siri and Google Now are already used by a large number of smartphone and tablet users. A major challenge in designing a voice control application is that it requires continuous monitoring of user's voice input through the microphone. Such applications utilize hotwords such as "Okay Google" or "Hi Galaxy" allowing them to distinguish user's voice command and her other conversations. A voice control application has to continuously listen for hotwords which significantly increases the energy consumption of the smart-devices.To address this energy e ciency problem of voice control, we present AccelWord in this paper. AccelWord is based on the empirical evidence that accelerometer sensors found in today's mobile devices are sensitive to user's voice. We also demonstrate that the e↵ect of user's voice on accelerometer data is rich enough so that it can be used to detect the hotwords spoken by the user. To achieve the goal of low energy cost but high detection accuracy, we combat multiple challenges, e.g. how to extract unique signatures of user's speaking hotwords only from accelerometer data and how to reduce the interference caused by user's mobility.We finally implement AccelWord as a standalone application running on Android devices. Comprehensive tests show AccelWord has hotword detection accuracy of 85% in static scenarios and 80% in mobile scenarios. Compared to the microphone based hotword detection applications such as Google Now and Samsung S Voice, AccelWord is 2 times more energy e cient while achieving the accuracy of 98% and 92% in static and mobile scenarios respectively.

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Noise is a common problem in wearable electrocardiogram (ECG) monitoring systems because the presence of noise can corrupt the ECG waveform causing inaccurate signal interpretation. By comparison with electromagnetic interference and its minimization, the reduction of motion artifact is more difficult and challenging because its time-frequency characteristics are unpredictable. Based on the characteristics of motion artifacts, this work uses adaptive filtering, a specially designed ECG device, and an Impedance Pneumography (IP) data acquisition system to combat motion artifacts. The newly designed ECG-IP acquisition system maximizes signal correlation by measuring both ECG and IP signals simultaneously using the same pair of electrodes. Signal comparison investigations between ECG and IP signals under five different body motions were carried out, and the Pearson Correlation Coefficient |r| was higher than 0.6 in all cases, indicating a good correlation. To optimize the performance of adaptive motion artifact reduction, the IP signal was filtered to a 5 Hz low-pass filter and then fed into a Recursive Least Squares (RLS) adaptive filter as a reference input signal. The performance of the proposed motion artifact reduction method was evaluated subjectively and objectively, and the results proved that the method could suppress the motion artifacts and achieve minimal distortion to the denoised ECG signal.