SMASH: a distributed sensing and processing garment for the classification of upper body postures

This paper introduces a smart textile for posture classification. A distributed sensing and processing architecture is implemented into a loose fitting long sleeve shirt. Standardized interfaces to remote periphery support the variable placement of different sensor modalities at any location of the textile. The shirt is equipped with acceleration sensors in order to determine the postural resolution and the systems feasibility for applications in movement rehabilitation. For the garment characterization an arm posture measurement method is proposed and applied in a study with 5 users. The classification performance is analyzed on data from overall 8 users, conducting 12 posture types, relevant for shoulder and elbow joint rehabilitation. We present results for different user-modes, with classification rates of 89% for a user-independent evaluation. Moreover, the relation of body dimensions on the posture classification performance are analyzed.

[1]  Alex Pentland,et al.  Real-time American Sign Language recognition from video using hidden Markov models , 1995 .

[2]  Robert B. McGhee,et al.  Sourceless tracking of human posture using small inertial/magnetic sensors , 2003, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694).

[3]  Paul Lukowicz,et al.  PadNET: wearable physical activity detection network , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[4]  Agnès Just,et al.  Hand Posture Classification and Recognition using the Modified Census Transform , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[5]  Kenneth Mackenzie,et al.  The wearable motherboard: a framework for personalized mobile information processing (PMIP) , 2002, DAC '02.

[6]  Kristofer S. J. Pister,et al.  Acceleration sensing glove (ASG) , 1999, Digest of Papers. Third International Symposium on Wearable Computers.

[7]  Lucy E. Dunne,et al.  Design and Evaluation of a Wearable Optical Sensor for Monitoring Seated Spinal Posture , 2006, 2006 10th IEEE International Symposium on Wearable Computers.

[8]  D. Rossi,et al.  Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation , 2005, First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics Conference.

[9]  S. Quaglini,et al.  Posture Classification via Wearable Strain Sensors for Neurological Rehabilitation , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Alex Pentland,et al.  Recognizing user context via wearable sensors , 2000, Digest of Papers. Fourth International Symposium on Wearable Computers.

[11]  Gerhard Tröster,et al.  Recognizing Upper Body Postures using Textile Strain Sensors , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[12]  Robert B. McGhee,et al.  An extended Kalman filter for quaternion-based orientation estimation using MARG sensors , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[13]  Kristofer S. J. Pister,et al.  Acceleration Sensing Glove , 1999 .

[14]  Peter H. Veltink,et al.  Inertial and magnetic sensing of human movement near ferromagnetic materials , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..