Friedrich, Petra
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- Living Lab AAL (13)
- CARE REGIO (8)
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This mixed-methods study aimed to investigate to what extent two-week device-supported cycle training and app-based fall risk assessment are feasible measures for fall prevention in older people living at home (N = 10). A questionnaire assessed participants’ motivation, and a fall risk score was calculated pre- and post-intervention. Semi-structured interviews with two experts were conducted retrospectively. The results revealed a high subjective motivation during the training and a slight reduction of the fall risk. Both the training and the assessment are feasible and well-received interventions with potential for technology-supported fall prevention.
Audio Feedback for Device-Supported Balance Training: Parameter Mapping and Influencing Factors
(2020)
Recent studies suggest that real-time auditory feedback is an effective method to facilitate motor learning. The evaluation of the parameter mapping (sound-to-movement mapping) is a crucial, yet frequently neglected step in the development of audio feedback. We therefore conducted two experiments to evaluate audio parameters with target finding exercises designed for balance training. In the first experiment with ten participants, five different audio parameters were evaluated on the X-axis (mediolateral movement). Following that, in a larger experiment with twenty participants in a two-dimensional plane (mediolateral and anterior-posterior movement), a basic and synthetic audio model was compared to a more complex audio model with musical characteristics. Participants were able to orient themselves and find the targets with the audio models. In the one-dimensional condition of experiment one, percussion sounds and synthetic sound wavering were the overall most effective audio parameters. In experiment two, the synthetic model was more effective and better evaluated by the participants. In general, basic sounds were more helpful than complex (musical) sound models. Musical abilities and age were correlated with certain exercise scores. Audio feedback is a promising approach for balance training and should be evaluated with patients. Preliminary evaluation of the respective parameter mapping is highly advisable.
In den letzten Jahren wurde ein immer größeres Spektrum von Assistenztechnologien entwickelt, um die Bereitstellung effektiver und effizienter Sturzpräventionsmaßnahmen zu unterstützen. Unzureichende oder fehlende Klassifizierungen technikgestützter Sturzprävention erschweren jedoch oft eine strukturierte Suche, Einordung und Vergleichbarkeit. Zwar gibt es eine Reihe von Artikeln, in denen Technologien für einen bestimmten Teilbereich der Sturzprävention untersucht wurden, es fehlen jedoch deutschsprachige Übersichten, die das breite Spektrum der Sturzpräventionsmaßnahmen untersuchen und die wichtigsten Aspekte der Technologieanwendung verständlich beschreiben. Dieser Beitrag soll einen Überblick mit einer praktikablen Klassifizierung und Beschreibung unterstützender Technologien zur Sturzprävention geben. Die Kategorien und Unterkategorien wurden aus einer Analyse von Studien zwischen 2010 und 2021 abgeleitet.
Background : Acoustic signals can modulate metabolic and central nervous functions and evoke physiological effects within humans. Especially, the anti-hypertensive effect of certain iterative sound patterns as possible intervention in essential hypertension, which originates in many cases from stress, is examined with COMES ® , the Cognitive Medical System based on telemedicine.
Methods : This contribution describes the structure, the mode of operation and introduces the detailed results of the prospective observational study ( N = 20) of musical feedback in hypertension therapy.
Results : It is shown that the music used for intervention can decrease blood pressure significantly ( t -test). The reduction averages −2.57 mmHg ( p = 0.013) RRsys, −1.54 mmHg ( p = 0.003) RRdia. This is comparable with pharmaceutical interventions. Discussion : COMES offers a great number of application options in diagnostics and therapy including an automatic intervention channel. It is possible to gather authentic data patterns in the patient’s personal environment and thus to obtain an immediate, realistic impression of arbitrary intervention structures. Therapists get further instruments to involve their patients actively into the therapy process at home. The measurement of various physiological parameters allows both patient and therapist a direct feedback on the efficacy of therapeutic intervention or relaxation exercises.