Weight estimation system using surface EMG armband

Roberto Oboe, Alessandro Tonin, Koyo Yu, Kouhei Ohnishi, Andrea Turolla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)


Knowing the force exerted by a human operator while he/she is performing a specific task is important in many different field. For instance, limiting or optimizing the effort in sport activities allows for the development of specific training patterns for athletes, while knowing the effort made by a worker when he/she lifts a weight is important from the point of view of the safety. The effective effort is not only related to the net force/torque generated, but also to the force generated by each muscle. This aspect is the most crucial to be evaluated, as a non properly designed/handled exercise/task can lead to an excessive muscle strain and, in turn, to injuries. In this paper we report some preliminary results obtained by using low-cost wearable sensors in the estimation of the weight lifted by a human operator, through the simultaneous measurement of motion (via inertial sensors) and the muscles activations (via surface ElectroMyoGraphy - sEMG). The armband has 8 sEMG sensors and a 9-DoF inertial sensor, it has electrically safe setup with low voltage battery and Bluetooth protocol. The relationship between the EMG and inertial signals and the exerted force was made using a biarticular model of the arm. The model was used in order to have a theoretical value of the shoulder and elbow torques performing a weightlifting standardized tests. The value was then compared with the one estimated by the identification of a model and by means of a neural network. In both cases, the results show the relationship between signals and torque, but, in both cases, the results are affected by error. Nevertheless, even if it doesnt accurately estimate the weight lifted, both the presented techniques highlight the possibility of developing a classification of the exerted force that, calibrating the system for each person, can identify whether the weight lifted is light or heavy.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Industrial Technology, ICIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509053209
Publication statusPublished - 2017 Apr 26
Event2017 IEEE International Conference on Industrial Technology, ICIT 2017 - Toronto, Canada
Duration: 2017 Mar 232017 Mar 25

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology


Other2017 IEEE International Conference on Industrial Technology, ICIT 2017


  • SEMG
  • Wearable devices
  • Weight lifting estimation

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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