Tap 'N' Shake: Gesture-based Smartwatch-Smartphone Communications System

Smartwatches have recently seen a surge in popularity, and the new technology presents a number of interesting opportunities and challenges, many of which have not been adequately dealt with by existing applications. Current smartwatch messaging systems fail to adequately address the problem of smartwatches requiring two-handed interactions. This paper presents Tap 'n' Shake, a novel gesture-based messaging system for Android smartwatches and smartphones addressing the problem of two-handed interactions by utilising various motion-gestures within the applications. The results of a user evaluation carried out with sixteen subjects demonstrated the usefulness and usability of using gestures over two-handed interactions for smartwatches. Additionally, the study provides insight into the types of gestures that subjects preferred to use for various actions in a smartwatch-smartphone messaging system

Tap'n'shake:gesture-based smartwatch-smartphone communications system

Smartwatches have recently seen a surge in popularity, and the new technology presents a number of interesting opportunities and challenges, many of which have not been adequately dealt with by existing applications. Current smartwatch messaging systems fail to adequately address the problem of smartwatches requiring two-handed interactions. This paper presents Tap 'n' Shake, a novel gesture-based messaging system for Android smartwatches and smartphones addressing the problem of two-handed interactions by utilising various motion-gestures within the applications. The results of a user evaluation carried out with sixteen subjects demonstrated the usefulness and usability of using gestures over two-handed interactions for smartwatches. Additionally, the study provides insight into the types of gestures that subjects preferred to use for various actions in a smartwatch-smartphone messaging system

Exploring user-defined gestures for alternate interaction space for smartphones and smartwatches

2016 Spring.Includes bibliographical references.In smartphones and smartwatches, the input space is limited due to their small form factor. Although many studies have highlighted the possibility of expanding the interaction space for these devices, limited work has been conducted on exploring end-user preferences for gestures in the proposed interaction spaces. In this dissertation, I present the results of two elicitation studies that explore end-user preferences for creating gestures in the proposed alternate interaction spaces for smartphones and smartwatches. Using the data collected from the two elicitation studies, I present gestures preferred by end-users for common tasks that can be performed using smartphones and smartwatches. I also present the end-user mental models for interaction in proposed interaction spaces for these devices, and highlight common user motivations and preferences for suggested gestures. Based on the findings, I present design implications for incorporating the proposed alternate interaction spaces for smartphones and smartwatches

Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel

Wrist-wearables such as smartwatches and fitness bands are equipped with a variety of high-precision sensors that support novel contextual and activity-based applications. The presence of a diverse set of on-board sensors, however, also expose an additional attack surface which, if not adequately protected, could be potentially exploited to leak private user information. In this paper, we investigate the feasibility of a new attack that takes advantage of a wrist-wearable's motion sensors to infer input on mechanical devices typically used to secure physical access, for example, combination locks. We outline an inference framework that attempts to infer a lock's unlock combination from the wrist motion captured by a smartwatch's gyroscope sensor, and uses a probabilistic model to produce a ranked list of likely unlock combinations. We conduct a thorough empirical evaluation of the proposed framework by employing unlocking-related motion data collected from human subject participants in a variety of controlled and realistic settings. Evaluation results from these experiments demonstrate that motion data from wrist-wearables can be effectively employed as a side-channel to significantly reduce the unlock combination search-space of commonly found combination locks, thus compromising the physical security provided by these locks

Indutivo: Contact-Based, Object-Driven Interactions with Inductive Sensing

We present Indutivo, a contact-based inductive sensing technique for contextual interactions. Our technique recognizes conductive objects (metallic primarily) that are commonly found in households and daily environments, as well as their individual movements when placed against the sensor. These movements include sliding, hinging, and rotation. We describe our sensing principle and how we designed the size, shape, and layout of our sensor coils to optimize sensitivity, sensing range, recognition and tracking accuracy. Through several studies, we also demonstrated the performance of our proposed sensing technique in environments with varying levels of noise and interference conditions. We conclude by presenting demo applications on a smartwatch, as well as insights and lessons we learned from our experience

WearPut : Designing Dexterous Wearable Input based on the Characteristics of Human Finger Motions

Department of Biomedical Engineering (Human Factors Engineering)Powerful microchips for computing and networking allow a wide range of wearable devices to be miniaturized with high fidelity and availability. In particular, the commercially successful smartwatches placed on the wrist drive market growth by sharing the role of smartphones and health management. The emerging Head Mounted Displays (HMDs) for Augmented Reality (AR) and Virtual Reality (VR) also impact various application areas in video games, education, simulation, and productivity tools. However, these powerful wearables have challenges in interaction with the inevitably limited space for input and output due to the specialized form factors for fitting the body parts. To complement the constrained interaction experience, many wearable devices still rely on other large form factor devices (e.g., smartphones or hand-held controllers). Despite their usefulness, the additional devices for interaction can constrain the viability of wearable devices in many usage scenarios by tethering users' hands to the physical devices. This thesis argues that developing novel Human-Computer interaction techniques for the specialized wearable form factors is vital for wearables to be reliable standalone products. This thesis seeks to address the issue of constrained interaction experience with novel interaction techniques by exploring finger motions during input for the specialized form factors of wearable devices. The several characteristics of the finger input motions are promising to enable increases in the expressiveness of input on the physically limited input space of wearable devices. First, the input techniques with fingers are prevalent on many large form factor devices (e.g., touchscreen or physical keyboard) due to fast and accurate performance and high familiarity. Second, many commercial wearable products provide built-in sensors (e.g., touchscreen or hand tracking system) to detect finger motions. This enables the implementation of novel interaction systems without any additional sensors or devices. Third, the specialized form factors of wearable devices can create unique input contexts while the fingers approach their locations, shapes, and components. Finally, the dexterity of fingers with a distinctive appearance, high degrees of freedom, and high sensitivity of joint angle perception have the potential to widen the range of input available with various movement features on the surface and in the air. Accordingly, the general claim of this thesis is that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. This thesis demonstrates the general claim by providing evidence in various wearable scenarios with smartwatches and HMDs. First, this thesis explored the comfort range of static and dynamic touch input with angles on the touchscreen of smartwatches. The results showed the specific comfort ranges on variations in fingers, finger regions, and poses due to the unique input context that the touching hand approaches a small and fixed touchscreen with a limited range of angles. Then, finger region-aware systems that recognize the flat and side of the finger were constructed based on the contact areas on the touchscreen to enhance the expressiveness of angle-based touch input. In the second scenario, this thesis revealed distinctive touch profiles of different fingers caused by the unique input context for the touchscreen of smartwatches. The results led to the implementation of finger identification systems for distinguishing two or three fingers. Two virtual keyboards with 12 and 16 keys showed the feasibility of touch-based finger identification that enables increases in the expressiveness of touch input techniques. In addition, this thesis supports the general claim with a range of wearable scenarios by exploring the finger input motions in the air. In the third scenario, this thesis investigated the motions of in-air finger stroking during unconstrained in-air typing for HMDs. The results of the observation study revealed details of in-air finger motions during fast sequential input, such as strategies, kinematics, correlated movements, inter-fingerstroke relationship, and individual in-air keys. The in-depth analysis led to a practical guideline for developing robust in-air typing systems with finger stroking. Lastly, this thesis examined the viable locations of in-air thumb touch input to the virtual targets above the palm. It was confirmed that fast and accurate sequential thumb touch can be achieved at a total of 8 key locations with the built-in hand tracking system in a commercial HMD. Final typing studies with a novel in-air thumb typing system verified increases in the expressiveness of virtual target selection on HMDs. This thesis argues that the objective and subjective results and novel interaction techniques in various wearable scenarios support the general claim that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. Finally, this thesis concludes with thesis contributions, design considerations, and the scope of future research works, for future researchers and developers to implement robust finger-based interaction systems on various types of wearable devices.ope

BRAILLESHAPES : efficient text input on smartwatches for blind people

Tese de Mestrado, Engenharia Informática, 2023, Universidade de Lisboa, Faculdade de CiênciasMobile touchscreen devices like smartphones or smartwatches are a predominant part of our lives. They have evolved, and so have their applications. Due to the constant growth and advancements in technology, using such devices as a means to accomplish a vast amount of tasks has become common practice. Nonetheless, relying on touch-based interactions, requiring good spatial ability and memorization inherent to mobile devices, and lacking sufficient tactile cues, makes these devices visually demanding, thus providing a strenuous interaction modality for visually impaired people. In scenarios occurring in movement-based contexts or where onehanded use is required, it is even more apparent. We believe devices like smartwatches can provide numerous advantages when addressing such topics. However, they lack accessible solutions for several tasks, with most of the existing ones for mobile touchscreen devices targeting smartphones. With communication being of the utmost importance and intrinsic to humankind, one task, in particular, for which it is imperative to provide solutions addressing its surrounding accessibility concerns is text entry. Since Braille is a reading standard for blind people and provided positive results in prior work regarding accessible text entry approaches, we believe using it as the basis for an accessible text entry solution can help solidify a standardization for this type of interaction modality. It can also allow users to leverage previous knowledge, reducing possible extra cognitive load. Yet, even though Braille-based chording solutions achieved good results, due to the reduced space of the smartwatch’s touchscreen, a tapping approach is not the most feasible. Hence, we found the best option to be a gesture-based solution. Therefore, with this thesis, we explored and validated the concept and feasibility of Braille-based shapes as the foundation for an accessible gesture-based smartwatch text entry method for visually impaired people

RotoSwype : word-gesture typing using a ring

Funding: NSERC Discovery Grant #2018-05187, the Canada Foundation for Innovation Infrastructure Fund “Facility for Fully Interactive Physio-digital Spaces” (#33151), and Ontario Early Researcher Award #ER16-12-184.We propose RotoSwype, a technique for word-gesture typing using the orientation of a ring worn on the index finger. RotoSwype enables one-handed text-input without encumbering the hand with a device, a desirable quality in many scenarios, including virtual or augmented reality. The method is evaluated using two arm positions: with the hand raised up with the palm parallel to the ground; and with the hand resting at the side with the palm facing the body. A five-day study finds both hand positions achieved speeds of at least 14 words-per-minute (WPM) with uncorrected error rates near 1%, outperforming previous comparable techniques.Postprin

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models