1st Workshop on Data mining for Aging, Rehabilitation and Independent Assisted Living (ARIAL) @ICDM'17

According to a United Nation’s report on World Population Aging (2015), the number of people in the world aged 60 or over is projected to grow to 2.1 billion by year 2050. Aging can come with various complexities and challenges, such as frailty and decline in cognitive and mental health of a person. These changes can affect a person’s everyday life, resulting in decreased social participation, lack of physical activity, and vulnerability to injury and disability, that can be exacerbated by the occurrence of various acute health events, such strokes, or long term illnesses.

Assistive technology refers to any device, equipment or tool that is used to maintain, increase or improve the functional capabilities of older adults or persons with disabilities. The field of assistive technology amalgamates several multi-disciplinary areas including computer science, rehabilitation engineering, data mining, clinical studies, health care, and psychology. The idea of assistive technological solutions is to promote independent, active and healthy aging with a specific focus on older adults, especially with mild cognitive impairments.

Collecting health data using assistive technology devices is a challenging task. Mining useful information from the vast amount of health and activity-related data from older adults is important. These data and the results from data mining can help to build models that facilitate independent assisted living, promote healthy and active lifestyle, and manage rehabilitation routines effectively.

In this workshop, we invite previously unpublished and novel submissions in the following areas, but not limited to:

• Methods and protocols for data collection with older adult populations.
• Techniques for continuous streaming and monitoring of health and activity data for older adults.
• Methodologies for big data and large-scale data mining.
• Data curation, sharing and harmonization.
• Data analytics and visualization techniques for healthcare data.
• Machine learning techniques to identify abnormal behaviours and rare activities.
• Methods to detect harmful and life-threatening events in older adults such as falls, strokes, seizures, agitation, aggression, wandering.
• Older adults-centred Social Media Analytics.
• Interactive solutions for engaging older adults to help in socializing, memorizing, reducing isolation and promote healthy living.
• Use of wearable, vision, and ambient sensors or their fusion for detection of physical, cognitive and affective (emotional) disabilities or decline.
• Data mining challenges such as handling missing information, dealing with mixed, imbalanced and noisy data.
• Other innovative data mining approaches that can be translated to applications for older adult populations.