Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU’15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.
Background: Multiple sclerosis often leads to fatigue and changes in physical behavior (PB). Changes in PB are often assumed as a consequence of fatigue, but effects of interventions that aim to reduce fatigue by improving PB are not sufficient. Since the heterogeneous nature of MS related symptoms, levels of PB of fatigued patients at the start of interventions might vary substantially. Better understanding of the variability by identification of PB subtypes in fatigued patients may help to develop more effective personalized rehabilitation programs in the future. This study aimed to identify PB subtypes in fatigued patients with multiple sclerosis based on multidimensional PB outcome measures. Methods: Baseline accelerometer (Actigraph) data, demographics and clinical characteristics of the TREFAMS-ACE participants (n = 212) were used for secondary analysis. All patients were ambulatory and diagnosed with severe fatigue based on a score of ≥35 on the fatigue subscale of the Checklist Individual Strength (CIS20r). Fifteen PB measures were used derived from 7 day measurements with an accelerometer. Principal component analysis was performed to define key outcome measures for PB and two-step cluster analysis was used to identify PB types. Results: Analysis revealed five key outcome measures: percentage sedentary behavior, total time in prolonged moderate-to-vigorous physical activity, number of sedentary bouts, and two types of change scores between day parts (morning, afternoon and evening). Based on these outcomes three valid PB clusters were derived. Conclusions: Patients with severe MS-related fatigue show three distinct and homogeneous PB subtypes. These PB subtypes, based on a unique set of PB outcome measures, may offer an opportunity to design more individually-tailored interventions in rehabilitation.
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OBJECTIVE: The purpose of this study was to determine the impact of partial glossectomy (using the keyhole technique) on speech intelligibility, articulation, resonance and oromyofunctional behavior. PATIENTS AND METHODS: A partial glossectomy was performed in 4 children with Beckwith- Wiedemann syndrome between the ages of 0.5 and 3.1 years. An ENT assessment, a phonetic inventory, a phonemic and phonological analysis and a consensus perceptual evaluation of speech intelligibility, resonance and oromyofunctional behavior were performed. RESULTS: It was not possible in this study to separate the effects of the surgery from the typical developmental progress of speech sound mastery. Improved speech intelligibility, a more complete phonetic inventory, an increase in phonological skills, normal resonance and increased motor-oriented oral behavior were found in the postsurgical condition. The presence of phonetic distortions, lip incompetence and interdental tongue position were still present in the postsurgical condition. CONCLUSION: Speech therapy should be focused on correct phonetic placement and a motor-oriented approach to increase lip competence, and on functional tongue exercises and tongue lifting during the production of alveolars. Detailed analyses in a larger number of subjects with and without Beckwith-Wiedemann syndrome may help further illustrate the long-term impact of partial glossectomy.
