@Article{Zhou2006b,
  Title                    = {Inertial measurements of upper limb motion},
  Author                   = {Zhou, H. and Hu, H. and Tao, Y.},
  Journal                  = {Medical and Biological Engineering and Computing},
  Year                     = {2006},
  Number                   = {6},
  Pages                    = {479--487},
  Volume                   = {44},

  Abstract                 = {We present an inertial sensor based monitoring system for measuring upper limb movements in real time. The purpose of this study is to develop a motion tracking device that can be integrated within a home-based rehabilitation system for stroke patients. Human upper limbs are represented by a kinematic chain in which there are four joint variables to be considered: three for the shoulder joint and one for the elbow joint. Kinematic models are built to estimate upper limb motion in 3-D, based on the inertial measurements of the wrist motion. An efficient simulated annealing optimisation method is proposed to reduce errors in estimates. Experimental results demonstrate the proposed system has less than 5% errors in most motion manners, compared to a standard motion tracker.},
  Doi                      = {10.1007/s11517-006-0063-z},
  ISSN                     = {0140-0118 (Print) 1741-0444 (Online)},
  Keywords                 = {Inertial measurement - Stroke rehabilitation - Motion tracking - Upper limb - Simulated annealing},
  Publisher                = {Springer Berlin / Heidelberg},
  Review                   = {Position sensing of limbs. Double integration. Fitted to two-link kinematic model of shoulder and elbow. Used "monte Carlo Sampling" optimisation to avoid drifting issues. MCS is a technique known as simulated annealing, which is: "a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities)." - Wiki

Assume segment lengths are known. Uses R = Rinit + dR * dt, which they seem to get from double integration. They refered to another paper (Foxlin, 2004) for removal of gravity. How do they deal with integration error? They claim that the error is non-Gaussian, so can't use Kalman filtering. Instead, they use MCS, which sees to minimize the coordinate of elbow (x1, y1, z1) and the length of the upper arm (that is...x1^2 + y1^2 + z1^2 - L1^2 --> 0), and do the same for the lower arm, and optimize for it. 

Of course, when they tested it, there is a 3mm error max when stationary and 1.8cm error max when moving.},
  Subject_collection       = {Engineering},
  Timestamp                = {2010.07.08}
}

{"_id":"BFzjiYE6ZYndfDJXY","bibbaseid":"zhou-hu-tao-inertialmeasurementsofupperlimbmotion-2006","downloads":0,"creationDate":"2017-09-14T16:34:37.289Z","title":"Inertial measurements of upper limb motion","author_short":["Zhou, H.","Hu, H.","Tao, Y."],"year":2006,"bibtype":"article","biburl":"https://raw.githubusercontent.com/jfslin/jfslin.github.io/master/jf2lin.bib","bibdata":{"bibtype":"article","type":"article","title":"Inertial measurements of upper limb motion","author":[{"propositions":[],"lastnames":["Zhou"],"firstnames":["H."],"suffixes":[]},{"propositions":[],"lastnames":["Hu"],"firstnames":["H."],"suffixes":[]},{"propositions":[],"lastnames":["Tao"],"firstnames":["Y."],"suffixes":[]}],"journal":"Medical and Biological Engineering and Computing","year":"2006","number":"6","pages":"479--487","volume":"44","abstract":"We present an inertial sensor based monitoring system for measuring upper limb movements in real time. The purpose of this study is to develop a motion tracking device that can be integrated within a home-based rehabilitation system for stroke patients. Human upper limbs are represented by a kinematic chain in which there are four joint variables to be considered: three for the shoulder joint and one for the elbow joint. Kinematic models are built to estimate upper limb motion in 3-D, based on the inertial measurements of the wrist motion. An efficient simulated annealing optimisation method is proposed to reduce errors in estimates. Experimental results demonstrate the proposed system has less than 5% errors in most motion manners, compared to a standard motion tracker.","doi":"10.1007/s11517-006-0063-z","issn":"0140-0118 (Print) 1741-0444 (Online)","keywords":"Inertial measurement - Stroke rehabilitation - Motion tracking - Upper limb - Simulated annealing","publisher":"Springer Berlin / Heidelberg","review":"Position sensing of limbs. Double integration. Fitted to two-link kinematic model of shoulder and elbow. Used \"monte Carlo Sampling\" optimisation to avoid drifting issues. MCS is a technique known as simulated annealing, which is: \"a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities).\" - Wiki Assume segment lengths are known. Uses R = Rinit + dR * dt, which they seem to get from double integration. They refered to another paper (Foxlin, 2004) for removal of gravity. How do they deal with integration error? They claim that the error is non-Gaussian, so can't use Kalman filtering. Instead, they use MCS, which sees to minimize the coordinate of elbow (x1, y1, z1) and the length of the upper arm (that is...x1^2 + y1^2 + z1^2 - L1^2 --> 0), and do the same for the lower arm, and optimize for it. Of course, when they tested it, there is a 3mm error max when stationary and 1.8cm error max when moving.","subject_collection":"Engineering","timestamp":"2010.07.08","bibtex":"@Article{Zhou2006b,\n Title = {Inertial measurements of upper limb motion},\n Author = {Zhou, H. and Hu, H. and Tao, Y.},\n Journal = {Medical and Biological Engineering and Computing},\n Year = {2006},\n Number = {6},\n Pages = {479--487},\n Volume = {44},\n\n Abstract = {We present an inertial sensor based monitoring system for measuring upper limb movements in real time. The purpose of this study is to develop a motion tracking device that can be integrated within a home-based rehabilitation system for stroke patients. Human upper limbs are represented by a kinematic chain in which there are four joint variables to be considered: three for the shoulder joint and one for the elbow joint. Kinematic models are built to estimate upper limb motion in 3-D, based on the inertial measurements of the wrist motion. An efficient simulated annealing optimisation method is proposed to reduce errors in estimates. Experimental results demonstrate the proposed system has less than 5% errors in most motion manners, compared to a standard motion tracker.},\n Doi = {10.1007/s11517-006-0063-z},\n ISSN = {0140-0118 (Print) 1741-0444 (Online)},\n Keywords = {Inertial measurement - Stroke rehabilitation - Motion tracking - Upper limb - Simulated annealing},\n Publisher = {Springer Berlin / Heidelberg},\n Review = {Position sensing of limbs. Double integration. Fitted to two-link kinematic model of shoulder and elbow. Used \"monte Carlo Sampling\" optimisation to avoid drifting issues. MCS is a technique known as simulated annealing, which is: \"a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities).\" - Wiki\n\nAssume segment lengths are known. Uses R = Rinit + dR * dt, which they seem to get from double integration. They refered to another paper (Foxlin, 2004) for removal of gravity. How do they deal with integration error? They claim that the error is non-Gaussian, so can't use Kalman filtering. Instead, they use MCS, which sees to minimize the coordinate of elbow (x1, y1, z1) and the length of the upper arm (that is...x1^2 + y1^2 + z1^2 - L1^2 --> 0), and do the same for the lower arm, and optimize for it. \n\nOf course, when they tested it, there is a 3mm error max when stationary and 1.8cm error max when moving.},\n Subject_collection = {Engineering},\n Timestamp = {2010.07.08}\n}\n\n","author_short":["Zhou, H.","Hu, H.","Tao, Y."],"key":"Zhou2006b","id":"Zhou2006b","bibbaseid":"zhou-hu-tao-inertialmeasurementsofupperlimbmotion-2006","role":"author","urls":{},"keyword":["Inertial measurement - Stroke rehabilitation - Motion tracking - Upper limb - Simulated annealing"],"downloads":0},"search_terms":["inertial","measurements","upper","limb","motion","zhou","hu","tao"],"keywords":["inertial measurement - stroke rehabilitation - motion tracking - upper limb - simulated annealing"],"authorIDs":[],"dataSources":["iCsmKnycRmHPxmhBd"]}