Abstract
Intent recognition strategies for lower-limb assistive technologies (e.g., prostheses and exoskeletons) are typically limited to transitions from one terrain to another (e.g., level ground to stairs) occurring under anticipated cognitive states and in a straight path. However, such systems might be unable to robustly handle unanticipated transitions and those that do not occur in a straight line, posing high fall and injury risks for individuals suffering from cognitive and/or physical impairments. Understanding how classification of human locomotion is influenced by changes of direction and user anticipation is important. In this study, we examined the performance of linear discriminant analysis in continuous classification of straight-line walking, 45°changes of direction (crossover and sidestep cuts) and cuts to stair-ascent (i.e., 'mixed' transitions), performed under varied anticipatory conditions of able-bodied individuals. Accelerographic and gyroscopic signals of the lower- and upper-body were used as signal inputs. Relatively high accuracy levels were observed during cut-to-stair, compared to cuts. In addition, classification of unanticipated maneuvers was compared for differing amounts of anticipated and unanticipated training data. It was observed that not more than two bouts of a given transition were needed to provide significant improvement in recognition of unanticipated mixed transitions. However, substantial (5 bouts) unanticipated information was needed to deliver improved performance of cuts (without stair). This study has implications for enhancing the design of recognition systems that adapt to changes of direction and user cognition. These outcomes could inform robust handling of 'unknown' circumstances and deliver an increased level of user safety and confidence.
| Original language | English (US) |
|---|---|
| Title of host publication | BIOROB 2018 - 7th IEEE International Conference on Biomedical Robotics and Biomechatronics |
| Publisher | IEEE Computer Society |
| Pages | 972-977 |
| Number of pages | 6 |
| Volume | 2018-August |
| ISBN (Electronic) | 9781538681831 |
| DOIs | |
| State | Published - Oct 9 2018 |
| Event | 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BIOROB 2018 - Enschede, Netherlands Duration: Aug 26 2018 → Aug 29 2018 |
Other
| Other | 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BIOROB 2018 |
|---|---|
| Country | Netherlands |
| City | Enschede |
| Period | 8/26/18 → 8/29/18 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Biomedical Engineering
- Mechanical Engineering
Cite this
Continuous Classification of Locomotor Transitions Performed under Altered Cutting Style, Complexity and Anticipation. / Kazemimoghadam, Mahdieh; Li, Wentao; Fey, Nicholas.
BIOROB 2018 - 7th IEEE International Conference on Biomedical Robotics and Biomechatronics. Vol. 2018-August IEEE Computer Society, 2018. p. 972-977 8487746.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Continuous Classification of Locomotor Transitions Performed under Altered Cutting Style, Complexity and Anticipation
AU - Kazemimoghadam, Mahdieh
AU - Li, Wentao
AU - Fey, Nicholas
PY - 2018/10/9
Y1 - 2018/10/9
N2 - Intent recognition strategies for lower-limb assistive technologies (e.g., prostheses and exoskeletons) are typically limited to transitions from one terrain to another (e.g., level ground to stairs) occurring under anticipated cognitive states and in a straight path. However, such systems might be unable to robustly handle unanticipated transitions and those that do not occur in a straight line, posing high fall and injury risks for individuals suffering from cognitive and/or physical impairments. Understanding how classification of human locomotion is influenced by changes of direction and user anticipation is important. In this study, we examined the performance of linear discriminant analysis in continuous classification of straight-line walking, 45°changes of direction (crossover and sidestep cuts) and cuts to stair-ascent (i.e., 'mixed' transitions), performed under varied anticipatory conditions of able-bodied individuals. Accelerographic and gyroscopic signals of the lower- and upper-body were used as signal inputs. Relatively high accuracy levels were observed during cut-to-stair, compared to cuts. In addition, classification of unanticipated maneuvers was compared for differing amounts of anticipated and unanticipated training data. It was observed that not more than two bouts of a given transition were needed to provide significant improvement in recognition of unanticipated mixed transitions. However, substantial (5 bouts) unanticipated information was needed to deliver improved performance of cuts (without stair). This study has implications for enhancing the design of recognition systems that adapt to changes of direction and user cognition. These outcomes could inform robust handling of 'unknown' circumstances and deliver an increased level of user safety and confidence.
AB - Intent recognition strategies for lower-limb assistive technologies (e.g., prostheses and exoskeletons) are typically limited to transitions from one terrain to another (e.g., level ground to stairs) occurring under anticipated cognitive states and in a straight path. However, such systems might be unable to robustly handle unanticipated transitions and those that do not occur in a straight line, posing high fall and injury risks for individuals suffering from cognitive and/or physical impairments. Understanding how classification of human locomotion is influenced by changes of direction and user anticipation is important. In this study, we examined the performance of linear discriminant analysis in continuous classification of straight-line walking, 45°changes of direction (crossover and sidestep cuts) and cuts to stair-ascent (i.e., 'mixed' transitions), performed under varied anticipatory conditions of able-bodied individuals. Accelerographic and gyroscopic signals of the lower- and upper-body were used as signal inputs. Relatively high accuracy levels were observed during cut-to-stair, compared to cuts. In addition, classification of unanticipated maneuvers was compared for differing amounts of anticipated and unanticipated training data. It was observed that not more than two bouts of a given transition were needed to provide significant improvement in recognition of unanticipated mixed transitions. However, substantial (5 bouts) unanticipated information was needed to deliver improved performance of cuts (without stair). This study has implications for enhancing the design of recognition systems that adapt to changes of direction and user cognition. These outcomes could inform robust handling of 'unknown' circumstances and deliver an increased level of user safety and confidence.
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UR - http://www.scopus.com/inward/citedby.url?scp=85056594649&partnerID=8YFLogxK
U2 - 10.1109/BIOROB.2018.8487746
DO - 10.1109/BIOROB.2018.8487746
M3 - Conference contribution
AN - SCOPUS:85056594649
VL - 2018-August
SP - 972
EP - 977
BT - BIOROB 2018 - 7th IEEE International Conference on Biomedical Robotics and Biomechatronics
PB - IEEE Computer Society
ER -