8th International Conference on Body Area Networks

Research Article

Unobtrusive Assessment of Bipedal Balance Performance

  • @INPROCEEDINGS{10.4108/icst.bodynets.2013.253532,
        author={Rolf Adelsberger and Gerhard Tr\o{}ster},
        title={Unobtrusive Assessment of Bipedal Balance Performance},
        proceedings={8th International Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2013},
        month={10},
        keywords={sensor gait balance stabilogram functional gait analysis fga embedded},
        doi={10.4108/icst.bodynets.2013.253532}
    }
    
  • Rolf Adelsberger
    Gerhard Tröster
    Year: 2013
    Unobtrusive Assessment of Bipedal Balance Performance
    BODYNETS
    ACM
    DOI: 10.4108/icst.bodynets.2013.253532
Rolf Adelsberger1,*, Gerhard Tröster1
  • 1: ETH Zurich
*Contact email: rolf.adelsberger@ife.ee.ethz.ch

Abstract

Reduced postural stability are symptoms of many medical conditions. Depending on the conditions, there are training or medication strategies to ameliorate the balance of a subject. After medical treatment (surgery etc.) or in the context of an intervention/recovery strategy, these patients are often assessed in functional gait assessments (FGA). An expert, e.g. a Physio Therapist, can decide based on the outcome of such an assessment on future treatment plans. FGAs are often performed with no technological assistance: a subject performs pre-de ned tasks and the performance is evaluated visually by an expert. Existing technological assessment tools are scarcely used due to time and monetary restrictions. In this paper, we present a wearable sensor system that can be used for FGAs. Our system comprises a pressure-sensing component and inertial sensors to assess features known to correlate with balance. We validated our system against technological state of the art. We used the system on 6 patients and 5 healthy subjects. The system can distinguish between normal stance and stance with reduced postural control with an accuracy of more than 93%. Walk- ing episodes were classi ed into two categories with 91%. Based on features of stance and features of gait, our system can discriminate between healthy subjects and subjects with reduced postural stability with an accuracy of 94%