4th International ICST Conference on Pervasive Computing Technologies for Healthcare

Research Article

Wearable assistant for load monitoring: recognition of on-body load placement from gait alterations

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  • @INPROCEEDINGS{10.4108/ICST.PERVASIVEHEALTH2010.8894,
        author={Marco Benocci and Marc Bachlin and Elisabetta Farella and Daniel Roggen and Luca Benini and Gerhard Troster},
        title={Wearable assistant for load monitoring: recognition of on-body load placement from gait alterations},
        proceedings={4th International ICST Conference on Pervasive Computing Technologies for Healthcare},
        proceedings_a={PERVASIVEHEALTH},
        year={2010},
        month={6},
        keywords={gait back pain load carriage wearable assistant accelerometer.},
        doi={10.4108/ICST.PERVASIVEHEALTH2010.8894}
    }
    
  • Marco Benocci
    Marc Bachlin
    Elisabetta Farella
    Daniel Roggen
    Luca Benini
    Gerhard Troster
    Year: 2010
    Wearable assistant for load monitoring: recognition of on-body load placement from gait alterations
    PERVASIVEHEALTH
    ICST
    DOI: 10.4108/ICST.PERVASIVEHEALTH2010.8894
Marco Benocci1,2,*, Marc Bachlin2, Elisabetta Farella1, Daniel Roggen3, Luca Benini4, Gerhard Troster3
  • 1: Electronics, Computer Sciences and Systems - DEIS, University of Bologna, Italy.
  • 2: Wearable Computing Lab, Swiss Federal Institute of Technology Zurich, Switzerland.
  • 3: Wearable Computing Lab, Swiss Federal Institute of Technology Zurich, Switzerland
  • 4: Electronics, Computer Sciences and Systems - DEIS, University of Bologna, Italy,
*Contact email: marco.benocci@unibo.it

Abstract

Daily life activities such as working and shopping may cause people to carry overloaded bags, frequently borne in an incorrect way (e.g. only on one shoulder, asymmetrically worn). When these activities alter the gait, back pain incidents can occur. Critical conditions can be monitored taking advantage from a wearable assistant, extracting contextual information by on-body acceleration signals. By acquiring data on trunk, limb and foot during gait, we are able to detect five walking tasks on loaded conditions: two-straps backpack carried on shoulders, backpack carried with a single strap on right and left shoulder, bag carried with the right and left hand. Seven subjects participated walking at self-selected speed on a treadmill carrying a load between 10-12% of their body weight. Subjects repeated each task for five times over three weeks. We classified the activities for a single user by use of KNN, naïve Bayes and SVM classifiers. KNN achieved the best recognition accuracy of 96.7% for day dependent classifier training. The sensors placement, which resulted to be different along consecutive days, affects performance evaluation: a +3° rotation on the coronal plane decreases the accuracy to 76.0%.