@article{TeipelHeineHeinetal.2017,
  author    = {Teipel, Stefan and Heine, Christina and Hein, Albert and Kr{\"u}ger, Frank and Kutschke, Andreas and Kernebeck, Sven and Halek, Margareta and Bader, Sebastian and Kirste, Thomas},
  title     = {Multidimensional assessment of challenging behaviors in advanced stages of dementia in nursing homes—The insideDEM framework},
  series = {Alzheimer's \& Dementia: Diagnosis, Assessment \& Disease Monitoring},
  volume    = {8},
  journal   = {Alzheimer's \& Dementia: Diagnosis, Assessment \& Disease Monitoring},
  issn      = {2352-8729},
  doi       = {10.25974/fhms-17330},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:836-opus-173308},
  pages     = {36 -- 44},
  year      = {2017},
  abstract  = {IntroductionAssessment of challenging behaviors in dementia is important for intervention selection. Here, we describe the technical and experimental setup and the feasibility of long-term multidimensional behavior assessment of people with dementia living in nursing homes.MethodsWe conducted 4 weeks of multimodal sensor assessment together with real-time observation of 17 residents with moderate to very severe dementia in two nursing care units. Nursing staff received extensive training on device handling and measurement procedures. Behavior of a subsample of eight participants was further recorded by videotaping during 4 weeks during day hours. Sensors were mounted on the participants' wrist and ankle and measured motion, rotation, as well as surrounding loudness level, light level, and air pressure.ResultsParticipants were in moderate to severe stages of dementia. Almost 100\% of participants exhibited relevant levels of challenging behaviors. Automated quality control detected 155 potential issues. But only 11\% of the recordings have been influenced by noncompliance of the participants. Qualitative debriefing of staff members suggested that implementation of the technology and observation platform in the routine procedures of the nursing home units was feasible and identified a range of user- and hardware-related implementation and handling challenges.DiscussionOur results indicate that high-quality behavior data from real-world environments can be made available for the development of intelligent assistive systems and that the problem of noncompliance seems to be manageable. Currently, we train machine-learning algorithms to detect episodes of challenging behaviors in the recorded sensor data.},
  language  = {en}
}
@article{GoerssHeinBaderetal.2019,
  author    = {Goerss, Doreen and Hein, Albert and Bader, Sebastian and Halek, Margareta and Kernebeck, Sven and Kutschke, Andreas and Kirste, Thomas and Teipel, Stefan J.},
  title     = {P1-284: AUTOMATED SENSOR-BASED DETECTION OF CHALLENGING BEHAVIORS IN ADVANCED STAGES OF DEMENTIA IN NURSING HOMES},
  series = {Alzheimer's \& Dementia},
  volume    = {15},
  journal   = {Alzheimer's \& Dementia},
  number    = {7S_Part_7},
  issn      = {1552-5260},
  doi       = {10.1016/j.jalz.2019.06.839},
  pages     = {P351},
  year      = {2019},
  language  = {en}
}
@article{GoerssHeinBaderetal.2019,
  author    = {Goerss, Doreen and Hein, Albert and Bader, Sebastian and Halek, Margareta and Kernebeck, Sven and Kutschke, Andreas and Kirste, Thomas and Teipel, Stefan J.},
  title     = {AUTOMATED SENSOR-BASED DETECTION OF CHALLENGING BEHAVIORS IN ADVANCED STAGES OF DEMENTIA IN NURSING HOMES},
  series = {Alzheimer's \& Dementia},
  volume    = {15},
  journal   = {Alzheimer's \& Dementia},
  issn      = {1552-5260},
  doi       = {10.1016/j.jalz.2019.06.4309},
  pages     = {P151 -- P152},
  year      = {2019},
  language  = {en}
}
@article{GoerssHeinBaderetal.2020,
  author    = {Goerss, Doreen and Hein, Albert and Bader, Sebastian and Halek, Margareta and Kernebeck, Sven and Kutschke, Andreas and Heine, Christina and Krueger, Frank and Kirste, Thomas and Teipel, Stefan},
  title     = {Automated sensor-based detection of challenging behaviors in advanced stages of dementia in nursing homes},
  series = {Alzheimer's \& Dementia},
  volume    = {16},
  journal   = {Alzheimer's \& Dementia},
  issn      = {1552-5260},
  doi       = {10.1016/j.jalz.2019.08.193},
  pages     = {672 -- 680},
  year      = {2020},
  abstract  = {Sensor-based assessment of challenging behaviors in dementia may be useful to support caregivers. Here, we investigated accelerometry as tool for identification and prediction of challenging behaviors. We set up a complex data recording study in two nursing homes with 17 persons in advanced stages of dementia. Study included four-week observation of behaviors. In parallel, subjects wore sensors 24 h/7 d. Participants underwent neuropsychological assessment including MiniMental State Examination and Cohen-Mansfield Agitation Inventory. We calculated the accelerometric motion score (AMS) from accelerometers. The AMS was associated with several types of agitated behaviors and could predict subject's Cohen-Mansfield Agitation Inventory values. Beyond the mechanistic association between AMS and behavior on the group level, the AMS provided an added value for prediction of behaviors on an individual level. We confirm that accelerometry can provide relevant information about challenging behaviors. We extended previous studies by differentiating various types of agitated behaviors and applying long-term measurements in a real-world setting.},
  language  = {mul}
}