@article{SeiffertHolsteinSchlosseretal.2017,
  author    = {Seiffert, Martin and Holstein, Flavio and Schlosser, Rainer and Schiller, Jochen},
  title     = {Next generation cooperative wearables},
  series = {IEEE access : practical research, open solutions},
  volume    = {5},
  journal   = {IEEE access : practical research, open solutions},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {Piscataway},
  issn      = {2169-3536},
  doi       = {10.1109/ACCESS.2017.2749005},
  pages     = {16793 -- 16807},
  year      = {2017},
  abstract  = {Currently available wearables are usually based on a single sensor node with integrated capabilities for classifying different activities. The next generation of cooperative wearables could be able to identify not only activities, but also to evaluate them qualitatively using the data of several sensor nodes attached to the body, to provide detailed feedback for the improvement of the execution. Especially within the application domains of sports and health-care, such immediate feedback to the execution of body movements is crucial for (re-) learning and improving motor skills. To enable such systems for a broad range of activities, generalized approaches for human motion assessment within sensor networks are required. In this paper, we present a generalized trainable activity assessment chain (AAC) for the online assessment of periodic human activity within a wireless body area network. AAC evaluates the execution of separate movements of a prior trained activity on a fine-grained quality scale. We connect qualitative assessment with human knowledge by projecting the AAC on the hierarchical decomposition of motion performed by the human body as well as establishing the assessment on a kinematic evaluation of biomechanically distinct motion fragments. We evaluate AAC in a real-world setting and show that AAC successfully delimits the movements of correctly performed activity from faulty executions and provides detailed reasons for the activity assessment.},
  language  = {en}
}
@article{HauptWolschkeRabeetal.2017,
  author    = {Haupt, T. and Wolschke, M. and Rabe, Sophie and Scholz, I. and Smurawski, A. and Salzwedel, Annett and Thomas, F. and Reich, H. and V{\"o}ller, Heinz and Liebach, J. and Eichler, Sarah},
  title     = {ReMove-It - Entwicklung einer telemedizinisch assistierten Bewegungstherapie f{\"u}r die Rehabilitation nach Intervention an der unteren Extremit{\"a}t},
  series = {B\&G Bewegungstherapie und Gesundheitssport},
  volume    = {33},
  journal   = {B\&G Bewegungstherapie und Gesundheitssport},
  number    = {5},
  publisher = {Thieme},
  address   = {Stuttgart},
  issn      = {1613-0863},
  doi       = {10.1055/s-0043-118139},
  pages     = {221 -- 226},
  year      = {2017},
  abstract  = {Knie- und H{\"u}ftgelenksarthrose z{\"a}hlen zu den zehn h{\"a}ufigsten Einzeldiagnosen in orthop{\"a}dischen Praxen. Die Wirksamkeit einer station{\"a}ren Rehabilitation f{\"u}r Patienten nach Knie- oder H{\"u}ft-Totalendoprothese (TEP) ist in mehreren Studien belegt. Dennoch stellt die mittel- und langfristige Nachhaltigkeit zum Erhalt des Therapieerfolges eine große Herausforderung dar. Das Ziel des Projekts ReMove-It ist es, einen Wirksamkeitsnachweis f{\"u}r eintelemedizinisch assistiertes Interventionstraining f{\"u}r Patienten nach einem operativen Eingriff an den unteren Extremit{\"a}ten zu erbringen. In dem Beitrag wird anhand von Erfahrungsberichten dargestellt, wie das interaktive {\"U}bungsprogramm f{\"u}r Knie- und H{\"u}ft-TEP-Patienten entwickelt und das telemedizinische Assistenzsystem MeineReha® in den Behandlungsalltag von drei Rehakliniken integriert wurde. Ebenso werden der Aufbau und Ablauf der klinischen Studie dargestellt und das System aus Sicht der beteiligten {\"A}rzte, und Therapeuten bewertet.},
  language  = {de}
}