Usability Challenges Of Elderly People Using Of Smart Phone Technology
With the increase in age and time the physical functions decreases and among elderly become more and more dependent on others, estimating approx. 18% of people with 65 years and above age. For them, smartphones have proved themselves to be very helpful since last decades. The success of smart phone technology has spread over a broad range of subscribers in western nations, from children to senior citizens, in a very short period of time. Traxler found in his research a great amount of people transforming their lives with the help of this technology. There has been more text exchanges between people, especially the younger generation, as compared to the face to face interactions.
Not only the younger generation but also the elderly people are found to use mobile phones on daily basis to endure contact with distant friends and relatives, for daily conversations. What we observe from this is that mobile phones are seen to be used as simple communication device. A mobile phone has its usability in a lot more different ways than only as a communication device. From the observations of elderly people interactions with the mobile interface, the need for a proper and clear feedback was observed. Therefore, the interface of the mobile applications has to be improved considering users and stakeholders. To improve the interface of mobile application to make it more usable for older adults the need to identify the usability challenges faced by older adults.
The usability challenges are broadly subdivided into 2 main classes Physical issues and Computer experience. Physical usability challenges like impaired ‘eyesight’, ‘haptic deterioration’ and ‘reduce hearing’ may create issues for the user to operate the existing mobile application therefore if the user is unable to identify the interface properly, each operation in the application will not be usable by older adults. Moreover, in computer experience the issues like ‘unfamiliar with interface’ and ‘limited understating of process’ these are amongst the common usability issues. The main challenges user face is in the GUI styles such as buttons, folder and menus. In comparison the younger user know everything about these terms and easy to maneuver on GUI, and older adults has been observed to face a great difficulty as they are mostly unaware of the concepts of latest GUI actions and task.
The use of mobile phones comes with its benefits and limitations. To meet the needs of the end-user, user-centered methodology is the most promising one. Human-centered design (HCD) supports a promising and durable methodology to understand user’s regular behavior, which helps design more spontaneous and easy to learn applications. HCD technology involves the end-user during the course of the product development and product testing process which ensures that all the needs of the end-user are met in terms of user experience and safety. It minimizes user’s rational load, releasing mental resources hence a better performance is achieved. Functional app interface produced with HCD technology can be used by the senior adults for long term clinical trials. Out of many methods to achieve a successful HCD productivity and to evaluate usability, one is think-aloud protocol, developed by Lewis in 1982. It inspires users to precisely express their views about what they are observing and feeling. Human-oriented researchers have overlooked the innovative technologies, because of which the wearable healthcare systems and devices have not been much explored with respect to user perspectives. This brings us to the discussion of the potential of the discipline of Machine Learning for more innovative and user perspective technologies.
Machine Learning (ML) today has proved itself to be the fastest growing technology which combines statistics with informatics and connects with knowledge discovery and data science. The hypothetical foundation of ML was explained by Thomas Bayes (1701–1761). Probabilistic interpretation has immensely influenced the artificial intelligence and statistical learning; inverse probability has permitted inferring the unknowns and making predictions. ML has been progressing a lot with the development of new algorithms, exploration of the data with low-cost computation. The implementation of data-intensive ML algorithms are applicable in health informatics, especially in the brain informatics research. Its application in biomedicine can be helpful in evidence-based decision-making and can further lead to personalized medicines.
