A Clustering Approach to Categorizing 7 Degree-of-Freedom Arm Motions during Activities of Daily Living

Spiers

IEEE Trans. Neural Syst. Rehabil. Eng.

2020

Self Cite

This paper is the first in a two-part series analyzing human arm and hand motion during a wide range of unstructured tasks. The wide variety of motions performed by the human arm during daily tasks makes it desirable to find representative subsets to reduce the dimensionality of these movements for a variety of applications, including the design and control of robotic and prosthetic devices. This paper presents a novel method and the results of an extensive human subjects study to obtain representative arm joint angle trajectories that span naturalistic motions during Activities of Daily Living (ADLs). In particular, we seek to identify sets of useful motion trajectories of the upper limb that are functions of a single variable, allowing, for instance, an entire prosthetic or robotic arm to be controlled with a single input from a user, along with a means to select between motions for different tasks. Data driven approaches are used to discover clusters and representative motion averages for the wrist 3 degree of freedom (DOF), elbow-wrist 4 DOF, and full-arm 7 DOF motions. The proposed method makes use of well-known techniques such as dynamic time warping (DTW) to obtain a divergence measure between motion segments, Ward's distance criterion to build hierarchical trees, and functional principal component analysis (fPCA) to evaluate cluster variability. The emerging clusters associate various recorded motions into primarily hand start and end location for the full-arm system, motion direction for the wrist-only system, and an intermediate between the two qualities for the elbow-wrist system.

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Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Three-, Four-, and Seven-Degree-of-Freedom Arm Movements

Spiers

IEEE Trans. Neural Syst. Rehabil. Eng.

2020

Self Cite

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“…Coordination between task location and hand orientations are then analyzed. All analysis methods used on the trajectory path data have been demonstrated to work well with similar data and are described in detail in previous work by the authors [28], and are therefore only briefly summarized.…”

Section: Discussionmentioning

confidence: 99%

“…Participants were chosen to span 24 to 71 years of age. The set of motions that were analyzed in this study are also the ones used in the first paper of this series [28], albeit with a completely different set of parameters analyzed. These motions are largely based on the functional measure AM-ULA [23], and are listed in Table 1, with the setup described in more detail in Fig.…”

Section: A Task Protocolmentioning

confidence: 99%

“…A clustering quality measure is computed by clustering all of the end-point and trajectory data, prior to averaging and using the same methods described above, and evaluating how often repetitions are clustered together at every merging [28]. A quality score is increased by one for every pair of repetitions that appear in the same cluster, and is computed for every number of clusters for each representation method.…”

Section: E Cluster Qualitymentioning

confidence: 99%

“…In the first paper of the series [28], we clustered 7 degree of freedom (DOF) joint angle trajectories of the arm; 3 DOF for the shoulder, 1 for the elbow, and 3 for the wrist. One major observation was that clusters seemingly depended strongly on hand location, agreeing with the spatial control hypothesis [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Decoupling Hand Location and Orientation

Spiers

IEEE Trans. Neural Syst. Rehabil. Eng.

2020

Self Cite

This paper is the second in a two-part series analyzing human arm and hand motion during a wide range of unstructured tasks. In this work, we track the hand of healthy individuals as they perform a variety of activities of daily living (ADLs) in three ways decoupled from hand orientation: end-point locations of the hand trajectory, whole path trajectories of the hand, and straight-line paths generated using start and end points of the hand. These data are examined by a clustering procedure to reduce the wide range of hand use to a smaller representative set. Hand orientations are subsequently analyzed for the end-point location clustering results and subsets of orientations are identified in three reference frames: global, torso, and forearm. Data driven methods that are used include dynamic time warping (DTW), DTW barycenter averaging (DBA), and agglomerative hierarchical clustering with Ward's linkage. Analysis of the endpoint locations, path trajectory, and straight-line path trajectory identified 5, 5, and 7 ADL task categories, respectively, while hand orientation analysis identified up to 4 subsets of orientations for each task location, discretized and classified to the facets of a rhombicuboctahedron. Together these provide insight into our hand usage in daily life and inform an implementation in prosthetic or robotic devices using sequential control.

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Trajectory Control for 3 Degree-of-Freedom Wrist Prosthesis in Virtual Reality: A Pilot Study

Bimbo

2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)

2020