Generating Arm-Swing Trajectories in Real-Time Using a Data-Driven Model for Gait Rehabilitation With Self-Selected Speed

Hejrati, Babak; Abbott, Jake J.

doi:10.1109/tnsre.2017.2740060

Cited by 12 publications

(10 citation statements)

References 33 publications

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“…Therefore, studying the swing angle fluctuation of the upper body is of great significance for determining the movement of the hand joint. Different from most research that used IMU to measure angular velocities of human body rods (Babak Hejrati et al, 2016;B. Hejrati, Merryweather, & Abbott, 2018;Shangjie et al, 2018), we explored the movement of upper limb joint angle during normal walking based on the marker point capture experiment.…”

Section: Discussionmentioning

confidence: 99%

“…Tanaka et al (Yuki, Tomoya, & Yuji, 2018) ever employed the equation for the elbow joint angle as the shoulder angle, it also verified the same cycle of shoulder joint and elbow joint. Hejrati et al (B. Hejrati et al, 2018) generated an arm-swing trajectory while a subject increased and then decreased walking velocity, as the subject slowed, the peak of arm angle decreased. This law can be verified relatively obvious in Fig.12 of Subject 1,3 and 4 when slow to stop.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The Human walking kinematic model combined with upper and lower limb movement

Chen¹,

Hong

Lin³

et al. 2021

Preprint

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The purpose of this study is to build a relatively complete human walking kinematic model. This model is combined with the rolling-foot model (lower limb) and multi-rod swinging model (upper limb) connected by COM. We calculated the velocity of COM and other critical joints of the upper limb by marker point capture experiment using the high-speed camera. This research shows that the hand joint velocity measured through the experiment can achieve high coincidence with that calculated by the theoretical model given specific inputs. Moreover, the common pattern of upper limb angles is also studied for an accurate description. The proposed kinematic model is expected to forecast desired motion intention for better compliance by the rehabilitation and assistive robots.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Human walking kinematic model combined with upper and lower limb movement

Chen¹,

Hong

Lin³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The role of arm swing in human gait has been extensively investigated [1], and benefits such as torso rotational stability and reduced energy expenditure during walking have been reported for proper arm swing [2], [3]. Importantly, evidence suggests the existence of neural couplings between the upper and lower limbs and points to the crucial role of appropriate arm swing in interlimb coordination and improving the central pattern generator's function during gait training.…”

Section: Introductionmentioning

confidence: 99%

“…While the mentioned studies have shown promise for increasing arm swing amplitude, the effects of providing rhythmic tactile feedback to adjust the arm swing cycle time are unexplored. Previous research has shown that the arm swing frequency is directly and highly correlated with its amplitude and also walking speed and cadence [2], [34]. In this work, we (1) present a lightweight, wearable, and wireless feedback system capable of providing synchronized vibrotactile feedback to the arms.…”

Section: Introductionmentioning

confidence: 99%

Modulation of Arm Swing Frequency and Gait Using Rhythmic Tactile Feedback

Noghani

Hossain

Hejrati

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Due to the neural coupling between upper and lower limbs and the importance of interlimb coordination in human gait, focusing on appropriate arm swing should be a part of gait rehabilitation in individuals with walking impairments. Despite its vital importance, there is a lack of effective methods to exploit the potential of arm swing inclusion for gait rehabilitation. In this work, we present a lightweight and wireless haptic feedback system that provides highly synchronized vibrotactile cues to the arms to manipulate arm swing and investigate the effects of this manipulation on the subjects' gait in a study with 12 participants (20-44 years). We found the developed system effectively adjusted the subjects' arm swing and stride cycle times by significantly reducing and increasing those parameters by up to 20% and 35%, respectively, compared to their baseline values during normal walking with no feedback. Particularly, the reduction of arms' and legs' cycle times translated into a substantial increase of up to 19.3% (on average) in walking speed. The response of the subjects to the feedback was also quantified in both transient and steady-state walking. The analysis of settling times from the transient responses revealed a fast and similar adaptation of both arms' and legs' movements to the feedback for reducing cycle time (i.e., increasing speed). Conversely, larger settling times and the time differences between arms' and legs' responses were observed due to feedback for increasing cycle times (i.e., reducing speed). The results clearly demonstrate the potential of the developed system to induce different arm-swing patterns as well as the ability of the proposed method to modulate key gait parameters through leveraging the interlimb neural coupling, with implications for gait training.

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“…In a study where the participants’ legs were suspended, in the majority of subjects, rhythmic motion of the arms in the antero-posterior direction elicited muscle activity and movement patterns in the legs similar to those created during walking [8]. Hejrati et al [9] created a data-driven linear time-invariant transfer function to predict the correct arm swing based on the realtime measurements of thighs’ angular velocity. The growing evidence for the role of arms in human locomotion has resulted in recommendations for their inclusion in rehabilitation or gait training of those with disrupted coordination abilities, such as stroke or Parkinson’s disease (PD) patients [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Modulation of Arm Swing Frequency and Gait Using Rhythmic Tactile Feedback

Noghani

Hossain

Hejrati

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Due to the neural coupling between upper and lower limbs and the importance of interlimb coordination in human gait, it has been recommended that focusing on appropriate arm swing should be a part of gait rehabilitation in individuals with walking impairments. Despite its vital importance, there is a lack of effective methods to exploit the potential of arm swing inclusion for gait rehabilitation. In this work, we present a lightweight and wireless haptic feedback system that provides highly synchronized vibrotactile cues to the arms to manipulate arm swing and investigate the effects of this manipulation on the subjects' gait in a study with 12 participants (20-44 years). We found the developed system effectively adjusted the subjects' arm swing and stride cycle times by significantly reducing and increasing those parameters by up to 20% and 35%, respectively, compared to their baseline values during normal walking with no feedback. Particularly, the reduction of arms' and legs' cycle times translated into a substantial increase of about 19.3% (on average) in walking speed. The response of the subjects to the feedback was also quantified in both transient and steady-state walking. The analysis of settling times from the transient responses revealed a fast and similar adaptation of both arms' and legs' movements to the feedback for reducing cycle time (i.e., increasing speed). Conversely, larger settling times and the time differences between arms' and legs' responses were observed due to feedback for increasing cycle times (i.e., reducing speed). The results clearly demonstrate the potential of the developed system to induce different arm-swing patterns as well as the ability of the proposed method to modulate key gait parameters through capitalizing on the interlimb neural coupling with implications for gait training.

show abstract

Generating Arm-Swing Trajectories in Real-Time Using a Data-Driven Model for Gait Rehabilitation With Self-Selected Speed

Cited by 12 publications

References 33 publications

The Human walking kinematic model combined with upper and lower limb movement

The Human walking kinematic model combined with upper and lower limb movement

Modulation of Arm Swing Frequency and Gait Using Rhythmic Tactile Feedback

Modulation of Arm Swing Frequency and Gait Using Rhythmic Tactile Feedback

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