Mobile Phone Text Entry

The rapid growth of Short Message Service (SMS) text messaging has meant that a fast and efficient text input method is a very important aspect of a mobile phone interface. The best way to test a new method is an empirical evaluation, but this is a time consuming and complex task. An alternative would be to predict the performance of a new method using a prediction model. Previous prediction models and evaluations of current text entry methods are discussed. The previous models have been shown to be highly inaccurate. A new prediction technique is presented that uses pilot study data and text analysis instead of the complex mathematical formulas of previous techniques. Predictions for the multi-press with next and T9 input methods are compared to the actual performance results of an empirical evaluation. The empirical evaluation also compares the performance of the newer T9 system to that of the more common multi-press method. T9 was significantly faster when entering sentences containing only dictionary words. However, the two methods were not significantly different when entering a mix of sentences that contained dictionary and non-dictionary words. As T9 remembers non-dictionary words that a user enters, it was concluded that T9 was the better method. Subjective data re- flected this with 60% of subjects stating that they preferred T9 over multi-press. The prediction technique was found to be too inaccurate to be useful to interface designers. The main failing of this and previous techniques appears to be the inability to accurately predict the mental preparation factor of the text entry actions. Further work to develop a method to calculate such values is needed

Pickup usability dominates: a brief history of mobile text entry research and adoption

Text entry on mobile devices (e.g. phones and PDAs) has been a research challenge since devices shrank below laptop size: mobile devices are simply too small to have a traditional full-size keyboard. There has been a profusion of research into text entry techniques for smaller keyboards and touch screens: some of which have become mainstream, while others have not lived up to early expectations. As the mobile phone industry moves to mainstream touch screen interaction we will review the range of input techniques for mobiles, together with evaluations that have taken place to assess their validity: from theoretical modelling through to formal usability experiments. We also report initial results on iPhone text entry speed

Comparing Evaluation Methods for Encumbrance and Walking on Interaction with Touchscreen Mobile Devices

In this paper, two walking evaluation methods were compared to evaluate the effects of encumbrance while the preferred walking speed (PWS) is controlled. Users frequently carry cumbersome objects (e.g. shopping bags) and use mobile devices at the same time which can cause interaction difficulties and erroneous input. The two methods used to control the PWS were: walking on a treadmill and walking around a predefined route on the ground while following a pacesetter. The results from our target acquisition experiment showed that for ground walking at 100% of PWS, accuracy dropped to 36% when carrying a bag in the dominant hand while accuracy reduced to 34% for holding a box under the dominant arm. We also discuss the advantages and limitations of each evaluation method when examining encumbrance and suggest treadmill walking is not the most suitable approach to use if walking speed is an important factor in future mobile studies

Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling

In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods

Investigating five key predictive text entry with combined distance and keystroke modelling

This paper investigates text entry on mobile devices using only five-keys. Primarily to support text entry on smaller devices than mobile phones, this method can also be used to maximise screen space on mobile phones. Reported combined Fitt's law and keystroke modelling predicts similar performance with bigram prediction using a five-key keypad as is currently achieved on standard mobile phones using unigram prediction. User studies reported here show similar user performance on five-key pads as found elsewhere for novice nine-key pad users

Using SMS text messaging for teaching and data collection in the behavioral sciences

Recent interest in university teaching has focused on interactivity in lectures and practical classes, and teachers in several fields have set up systems in which students can interact with the lecturer using mobile-phone-based SMS text messaging. This approach has particular potential in psychology, where students could use SMS messaging as a way of responding in simple psychology experiments or demonstrations. We describe a simple architecture for an SMS-based responding, using an SMS-to-HTTP message relay service, and a PHP/MySQL input-output handler. We describe briefly two experiments we have run using the system. The first experiment examined anchoring effects in an SMS-based auction. The second experiment examined delay discounting, with participants indicating their intertemporal preferences using SMS. Finally, we evaluate the feedback we obtained from students about the practical and conceptual issues surrounding text-message-based responding

An Open Source Testing Tool for Evaluating Handwriting Input Methods

This paper presents an open source tool for testing the recognition accuracy of Chinese handwriting input methods. The tool consists of two modules, namely the PC and Android mobile client. The PC client reads handwritten samples in the computer, and transfers them individually to the Android client in accordance with the socket communication protocol. After the Android client receives the data, it simulates the handwriting on screen of client device, and triggers the corresponding handwriting recognition method. The recognition accuracy is recorded by the Android client. We present the design principles and describe the implementation of the test platform. We construct several test datasets for evaluating different handwriting recognition systems, and conduct an objective and comprehensive test using six Chinese handwriting input methods with five datasets. The test results for the recognition accuracy are then compared and analyzed.Comment: 5 pages, 3 figures, 11 tables. Accepted to appear at ICDAR 201

Emerging Opportunities: Monitoring and Evaluation in a Tech-Enabled World

Various trends are impacting on the field of monitoring and evaluation in the area of international development. Resources have become ever more scarce while expectations for what development assistance should achieve are growing. The search for more efficient systems to measure impact is on. Country governments are also working to improve their own capacities for evaluation, and demand is rising from national and community-based organizations for meaningful participation in the evaluation process as well as for greater voice and more accountability from both aid and development agencies and government.These factors, in addition to greater competition for limited resources in the area of international development, are pushing donors, program participants and evaluators themselves to seek more rigorous – and at the same time flexible – systems to monitor and evaluate development and humanitarian interventions.However, many current approaches to M&E are unable to address the changing structure of development assistance and the increasingly complex environment in which it operates. Operational challenges (for example, limited time, insufficient resources and poor data quality) as well as methodological challenges that impact on the quality and timeliness of evaluation exercises have yet to be fully overcome