Parkinson's disease (PD) is often responsible for difficulties in interacting with smartphones; however, research has not yet addressed these issues and how these challenge people with Parkinson's (PwP). This paper specifically investigates the symptoms and characteristics of PD that may influence the interaction with smartphones to then contribute in this direction. The research was based on a literature review of PD symptoms, eight semi-structured interviews with healthcare professionals and observations of PwP, and usability experiments with 39 PwP. Contributions include a list of PD symptoms that may influence the interaction with smartphones, a set of experimental results that evaluated the performance of four gestures tap, swipe, multiple-tap, and drag and 12 user interface design guidelines for creating smartphone user interfaces for PwP. Findings contribute to the work of re-

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According to statistics, one in every three adults ageing 65 or older falls every year. Every fall may lead to long-term consequences due to fractures or even neurological damages. These consequences have severe impact in their quality of life, independence and confidence, ultimately increasing the risk of early death. Moreover, the risk of falling increases as age advances. Fortunately, several studies reveal that specific exercise programmes may help in reducing the risk of falling if performed correctly and frequently. However, user engagement and adherence to these programmes are still low mainly due to motivational factors, since interventions are usually long, unadapted and unchallenging. In this paper, a new solution is presented, which uses the concept of interactive games using motion sensors to tackle low adherence (through gaming motivation) and help in physical rehabilitation and reduce fall risk on elderly people by improving balance, muscle strength and mobility. It is intended to be used in community or domestic unsupervised contexts and supports relatively inexpensive sensing equipment (currently Kinect R , Leap Motion R , Orbotix Sphero R and Smartphones) and common platforms (desktop and mobile). Tests were already undertaken with several individuals ageing 65 or more and the results were analysed and discussed, being generally positive, despite some issues in the movement detection algorithms.

Parkinson's Disease is one of the most common neurodegenerative disorders of the central nervous system that affects elderly. There are six main symptoms: tremors, rigidity, bradykinesia (slow movements), hand asymmetry, posture instability and freezing of gait. Nowadays any type of diagnose for this disorder is done through observation by a health care professional specialized in this area. Therefore a simpler and more efficient method that General Practioners can use to have some grounded information to decide to forward a possible patient to a specialist is needed. With this in mind different systems were studied coming to the conclusion that a mobile application is among the best options. This work can be split in four important phases (see Figure 1): (1) study of the current market for this problem and for the solution to be developed, (2) development of a smartphone application capable of gathering data of the early symptoms of Parkinson's taking into consideration all the smartphone's specifications; (3) use the application to gather data from real patients and a control group and (4) test and select a classification algorithm. The first phase involved two research topics: problem and solution. The problem consisted in studying all the symptoms that could theoretically be detected by the different smartphone components. The solution consisted in studying the different methods used to solve such a problem using data mining techniques (different feature selection and classification algorithms that best take advantage of the nature of the data gathered). The second phase consisted in the development of the smartphone application with four components (spiral analysis, tap analysis, simple questions and gait analysis). The third phase was dedicated in building the control group gathering data from healthy people and a Parkinson patients group for a total of 35 subjects. Finally, the fourth phase was using the studied algorithms to filter the different features- and compare the different algorithms selected. With the available data from the test subjects it was possible to achieve promising results from the gait analysis of the patients where the pelvic sway was a good feature to help differentiate Parkinson patients from healthy ones

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