Control scheme selection in human-machine-interfaces by analysis of activity signals

Human-Machine Interfaces in rehabilitation engineering often use activity signals. Examples are electrical wheelchairs or prostheses controlled by means of muscle contractions. Activity signals are user-dependent and often reflect neurological weaknesses. Thus, not all users are able to operate the same control scheme in a robust manner. To avoid under- and overstraining, the interface ideally uses a control scheme which reflects the user’s control ability best. Therefore, we explored typical phenomena of activation signals. We derive criteria to quantify the user’s performance and abilities and present a routine which automatically selects and adapts one of three control schemes being best suited

Data-driven analysis of interactions between people with dementia and a tablet device

Abstract In the project I-CARE a technical system for tablet devices is developed that captures the personal needs and skills of people with dementia. The system provides activation content such as music videos, biographical photographs and quizzes on various topics of interest to people with dementia, their families and professional caregivers. To adapt the system, the activation content is adjusted to the daily condition of individual users. For this purpose, emotions are automatically detected through facial expressions, motion, and voice. The daily interactions of the users with the tablet devices are documented in log files which can be merged into an event list. In this paper, we propose an advanced format for event lists and a data analysis strategy. A transformation scheme is developed in order to obtain datasets with features and time series for popular methods of data mining. The proposed methods are applied to analysing the interactions of people with dementia with the I-CARE tablet device. We show how the new format of event lists and the innovative transformation scheme can be used to compress the stored data, to identify groups of users, and to model changes of user behaviour. As the I-CARE user studies are still ongoing, simulated benchmark log files are applied to illustrate the data mining strategy. We discuss possible solutions to challenges that appear in the context of I-CARE and that are relevant to a broad range of applications.</jats:p

Control scheme selection in human-machine- interfaces by analysis of activity signals

Human-Machine Interfaces in rehabilitation engineering often use activity signals. Examples are electrical wheelchairs or prostheses controlled by means of muscle contractions. Activity signals are user-dependent and often reflect neurological weaknesses. Thus, not all users are able to operate the same control scheme in a robust manner. To avoid under- and overstraining, the interface ideally uses a control scheme which reflects the user’s control ability best. Therefore, we explored typical phenomena of activation signals. We derive criteria to quantify the user’s performance and abilities and present a routine which automatically selects and adapts one of three control schemes being best suited

Data-driven analysis of interactions between people with dementia and a tablet device

In the project I-CARE a technical system for tablet devices is developed that captures the personal needs and skills of people with dementia. The system provides activation content such as music videos, biographical photographs and quizzes on various topics of interest to people with dementia, their families and professional caregivers. To adapt the system, the activation content is adjusted to the daily condition of individual users. For this purpose, emotions are automatically detected through facial expressions, motion, and voice. The daily interactions of the users with the tablet devices are documented in log files which can be merged into an event list. In this paper, we propose an advanced format for event lists and a data analysis strategy. A transformation scheme is developed in order to obtain datasets with features and time series for popular methods of data mining. The proposed methods are applied to analysing the interactions of people with dementia with the I-CARE tablet device. We show how the new format of event lists and the innovative transformation scheme can be used to compress the stored data, to identify groups of users, and to model changes of user behaviour. As the I-CARE user studies are still ongoing, simulated benchmark log files are applied to illustrate the data mining strategy. We discuss possible solutions to challenges that appear in the context of I-CARE and that are relevant to a broad range of applications