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Interfacing human and computer with wireless body area sensor networks: the WiMoCA solution

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Abstract

Wireless Body Area Sensor Networks (WBASN) are an emerging technology enabling the design of natural human–computer interfaces (HCI). Automatic recognition of human motion, gestures, and activities is studied in several contexts. For example, mobile computing technology is being considered as a replacement of traditional input systems. Moreover, body posture and activity monitoring can be used for entertainment and health-care applications. However, until now, little work has been done to develop flexible and efficient WBASN solutions suitable for a wide range of applications. Their requirements pose new challenges for sensor network designs, such as optimizing traditional solutions for use as environmental monitoring-like applications and developing on-the-field stress tests. In this paper, we demonstrate the flexibility of a custom-designed WBASN called WiMoCA with respect to a wide range of posture and activity recognition applications by means of practical implementation and on-the-field testing. Nodes of the network mounted on different parts of the human body exploit tri-axial accelerometers to detect its movements. The advanced digital Micro-electro-mechanical system (MEMS) based inertial sensor has been chosen for WiMoCA because it demonstrated high flexibility of use in many different situations, providing the chance to exploit both static and dynamic acceleration components for different purposes. Furthermore, the sensibility and accuracy of the sensing element is perfectly adequate for monitoring human movement, while keeping cost low and size compact, thus meeting our requirements. We implemented three types of applications, stressing the WBASN in many aspects. In fact, they are characterized by different requirements in terms of accuracy, timeliness, and computation distributed on sensing nodes. For each application, we describe its implementation, and we discuss results about performance and power consumption.

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References

  1. Aminian K, Andres ED, Rezakhanlou K, Fritsch C, Schutz Y, Depairon M, Leyvraz P-F, Robert P (1998) Motion analysis in clinical practice using ambulatory accelerometry. Lect Notes Comput Sci 1537:1–11

    Article  Google Scholar 

  2. Angesleva J, Oakley I, Hughes S, O’Modhrain S (2003) Body mnemonics: portable device interaction design concept. In: Proceedings of the 16th annual ACM symposium on user interface software and technology

  3. Bachmann E, Yun X, McKinney D, McGhee R, Zyda M (2003) Design and implementation of marg sensors for 3-dof orientation measurement of rigid bodies. In: Proceedings robotics and automation, 2003. ICRA ’03. IEEE international conference on pp 14–19, September

  4. Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Proc. of pervasive 2004, the second international conference on pervasive computing. Springer, pp 1–17

  5. Barbieri R, Farella E, Acquaviva A, Benini L, Riccó B (2004) A low-power motion capture system with integrated accelerometer. Consumer communications and networking conference, 2004. CCNC 2004. First IEEE, pp 418–423, January

  6. Brunelli D, Farella E, Rocchi L, Dozza M, Chiari L, Benini L (2006) Bio-feedback system for rehabilitation based on wireless body area network. In: In Proceedings of fourth IEEE international conference on pervasive computing (Percom06)—Workshop ubicare 2006, pp 527–531

  7. Bussmann J, Martens WLJ, Tulen JHM, Schasfoort FC, Berg-Emons HJGVD, Stam HJ (2001) Measuring daily behaviour using ambulatory accelerometry: the activity monitor. Behav Res Meth Instrum Comput 33(3):349–356

    Google Scholar 

  8. Caccamo M, Zhang LY, Sha L, Buttazzo GC (2002) An implicit prioritized access protocol for wireless sensor networks. In: IEEE real-time systems symposium, pp 39–48, December

  9. Cattaneo D, Ferrarin M, Frasson W, Casiraghi A (2005) Head control: volitional aspects of rehabilitation training in patients with multiple sclerosis compared with healthy subjects. Arch Phys Med Rehabil 86:1381–1388

    Article  Google Scholar 

  10. Cheok AD, Kumar KG, Prince S (2002) Micro-accelerometer based hardware interfaces for wearable computer mixed reality applications. In: Wearable computers, 2002. (ISWC 2002). Proceedings sixth international symposium on pp 223–230 October

  11. Chiari L, Dozza M, Cappello A, Horak FB, Macellari V, Giansanti D (2005) Audio-biofeedback for balance improvement: an accelerometry-based system. Biomed Eng IEEE Trans 52:2108–2111

    Article  Google Scholar 

  12. Coley B, Najafi B, Paraschiv-Ionescu A, Aminian K (2005) Stair climbing detection during daily physical activity using a miniature gyroscope. Gait Posture 22(4):287–294, December

    Article  Google Scholar 

  13. Culhane KM, O’Connor M, Lyons D, Lyons GM (2005) Accelerometers in rehabilitation medicine for older adults. Age Ageing 34(6):556–560

    Article  Google Scholar 

  14. Delac K, Grgic M (2004) A survey of biometric recognition methods. In: Electronics in marine, 2004. In: Proceedings Elmar 2004, 46th international symposium, pp 184–193, 16–18 June

  15. Faidley G, Hero J, Lee K, Lwakabamba B, Walstrom R, Chen F, Dickerson J, Rover D, Weber R, Cruz-Neira C (2004) Developing an integrated wireless system for fully immersive virtual reality environments. In: Wearable computers, 2004. ISWC 2004, Eighth international symposium on vol 1, pp 178–179, 31 October–3 November

  16. Farella E, Brunelli D, Benini L, Riccó B, Bonfigli ME (2005) Pervasive computing for interactive virtual heritage. Multim IEEE 12(3):46–58, July–September

    Article  Google Scholar 

  17. Farella E, OḾodhrain S, Benini L, Riccó B (2006) Gesture signature for ambient intelligence applications: a feasibility study. In: Proceedings Pervasive computing, 4th international conference, pervasive 2006. Dublin, Ireland, pp 288–304, 7–10 May

  18. Farella E, Pieracci A, Brunelli D, Benini L, Riccó B (2005) Design and implementation of wimoca node for a body area wireless sensor network. In: Proc. of systems communications 2005 IEEE SENET, pp 342–347, August

  19. Foxlin E, Harrington M (2000) Weartrack: a self-referenced head and hand tracker for wearable computers and portable vr. In: Wearable computers 2000, The fourth international symposium on pp 155–162, October

  20. Golding AR, Lesh N (1999) Indoor navigation using a diverse set of cheap, wearable sensors. In: ISWC ’99: Proceedings of the 3rd IEEE international symposium on wearable computers, pp 29–36, Washington, DC, USA. IEEE Computer Society

  21. Harada T, Uchino H, Mori T, Sato T (2003) Portable orientation estimation device based on accelerometers, magnetometers and gyroscope sensors for sensor network. In: Multisensor fusion and integration for intelligent systems, MFI2003. Proceedings of IEEE international conference on pp 191–196, July

  22. Headon R, Coulouris G (2003) Supporting gestural input for users on the move. In: Proceedings eurowearable, pp 107–112, September

  23. Jarnlo GB, Thorngren KG (1991) Standing balance in hip fracture patients. 20 middle-aged patients compared with 20 healthy subjects. Acta Orthop Scand 62:427–434

    Article  Google Scholar 

  24. Jovanov E, Milenkovic A, Otto C, de Groen PC (2005) A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. J NeuroEng Rehabil 2:6–16

    Article  Google Scholar 

  25. Junker H, Lukowicz P, Troster G (2003) Padnet: wearable physical activity detection network. In: ISWC ’03: Proceedings of the 7th IEEE international symposium on wearable computers, pp 244–245. Washington, DC, USA. IEEE Computer Society

  26. Kahn JM, Katz RH, Pister KSJ (2000) Emerging challenges: mobile networking for mart dust. J Commun 2(3):188

    Google Scholar 

  27. Kern N, Schiele B, Schmidt A (2003) Multi-sensor activity context detection for wearable computing. In: Proc. of european symposium on ambient intelligence, pp 220–232, November

  28. Lee S-W, Mase K (2001) Recognition of walking behaviors for pedestrian navigation. In: Control applications, 2001 (CCA ’01). Proceedings of the 2001 IEEE international conference on pp 1152–1155. IEEE Control Systems Soc

  29. Lee S-W, Mase K (2002) Activity and location recognition using wearable sensors. IEEE Pervasive Computing 1(3):24–32, July–Sept

    Article  Google Scholar 

  30. Maki BE, McIlroy WE (1996) Postural control in the older adult. Clin Geriatr Med 12:635–658

    Google Scholar 

  31. Mathie MJ, Coster ACF, Lovell NH, Celler BG (2004) Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiol Meas 25:R1–20

    Article  Google Scholar 

  32. Mäntyjärvi J, Lindholm M, Vildjiounaite E, Mäkelä S-M, Ailisto H (2005) Identifying users of portable devices from gait pattern with accelerometers. In: Acoustics, speech, and signal processing, 2005. Proceedings (ICASSP ’05). IEEE international conference on vol 2, pp ii/973–ii/976

  33. Morris S, Paradiso J (2002) Shoe-integrated sensor system for wireless gait analysis and real-time feedback. EMBS/BMES Conference, 2002. Proceedings of the second joint, vol. 3, pp 2468–2469, 23–26 October

  34. Oakley I, Angesleva J, Hughes S, O’Modhrain S (2004) Tilt and fill: scrolling with vibrotactile display. In: Proc. of euroHaptics, pp 313–323, June

  35. Paradiso J (2002) Footnotes: personal reflections on the development of instrumented dance shoes and their musical applications. In: Quinz E (ed) Digital performance, Anomalie 2:34–49

  36. Perng JK, Fisher B, Hollar S, Pister KS (1999) Acceleration sensing glove (ASG). In: IEEE symposium on wearable computers, pp 178–180, October

  37. Randell C, Muller H (2000) Context awareness by analyzing accelerometer data. In: ISWC ’00: Proceedings of the 4th IEEE international symposium on wearable computers, p 175, Washington, DC, USA. IEEE Computer Society

  38. Moe-Nilssen R (1998) A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: the instrument. Clin Biomech (Bristol, Avon) 13(4-5):320–327, June

    Article  Google Scholar 

  39. Rocchi L, Chiari L, Horak FB (2002) Effects of deep brain stimulation and levodopa on postural sway in Parkinson’s disease. J Neurol Neurosurg Psychiatry 73:267–274

    Article  Google Scholar 

  40. Sama M, Pacella V, Farella E, Benini L, Riccó B (2006) 3did: a low-power, low-cost hand motion capture device. In: Proceedings IEEE design, automation and test in europe conference and exhibition, 6–10 March 2006

  41. Schmit JM, Regis DI, Riley MA (2005) Dynamic patterns of postural sway in ballet dancers and track athletes. Exp Brain Res 163:370–378

    Article  Google Scholar 

  42. Stankovic J, Abdelzaher T, Lu C, Sha L, Hou J (2003) Real-time communication and coordination in embedded sensor networks. In: Proceedings of the IEEE, vol 91, pp 1002–1022, July

  43. Uswatte G, Miltner WHR, Foo B, Varma M, Moran S, Taub E (2000) Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter. Stroke 31(3):662–667, March

    Google Scholar 

  44. van Dam T, Langendoen K (2003) An adaptive energy-efficient mac protocol for wireless sensor networks. In: SenSys ’03: Proceedings of the 1st international conference on embedded networked sensor systems. ACM Press, pp 171–180

  45. Veltink PH, Bussmann HB, de Vries W, Martens WL, Lummel RCV (1996) Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans Rehabil Eng 4(4):375–85, December

    Article  Google Scholar 

  46. Winter DA, Prince F, Frank JS, Powell C, Zabjek KF (1996) Unified theory regarding A/P and M/L balance in quiet stance. J Neurophysiol 75:2334–2343

    Google Scholar 

  47. www.onbalance.com (2005) Ref Type: internet Communication

  48. Zhu R, Zhou Z (2004) A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. IEEE Trans Neural Syst Rehabil Eng 12(2):295–302

    Article  Google Scholar 

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Authors and Affiliations

  1. DEIS—University of Bologna, Bologna, Italy

    Elisabetta Farella, Augusto Pieracci, Luca Benini & Laura Rocchi

  2. ISTI—Urbino University, Urbino, Italy

    Andrea Acquaviva

Authors
  1. Elisabetta Farella
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  2. Augusto Pieracci
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  3. Luca Benini
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  4. Laura Rocchi
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  5. Andrea Acquaviva
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Correspondence to Elisabetta Farella.

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Farella, E., Pieracci, A., Benini, L. et al. Interfacing human and computer with wireless body area sensor networks: the WiMoCA solution. Multimed Tools Appl 38, 337–363 (2008). https://doi.org/10.1007/s11042-007-0189-5

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