Adaptive walking load control for training physical strength using cane-type robot

Itadera, Shunki; Hasegawa, Yasuhisa; Fukuda, Toshio; Tanimoto, Masanori; Kondo, Izumi

doi:10.1109/iros.2017.8202202

Cited by 7 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with wearable motion analysis equipment, the intelligent walking stick is suitable for people with mobility difficulties. To help those people to walk safely and independently, auxiliary intelligent walking sticks with sensors and algorithms were proposed in [21][22][23][24]. In [21], an intelligent walking stick was designed, which was equipped with a multi-sensor system to detect obstacles.…”

Section: Human Motion Analysis Of the Intelligent Walking Stickmentioning

confidence: 99%

“…A robotic cane was proposed in [22], which could help users to stand and maintain their balance while standing and walking. In [23,24], the intelligent sticks on admittance control were designed to provide safe and efficient gait training for the elderly. However, the above researches failed to consider the communication between caregivers and walking stick users, and the motion information could not be transmitted to the caregivers in real-time to achieve better nursing results.…”

Section: Human Motion Analysis Of the Intelligent Walking Stickmentioning

confidence: 99%

See 1 more Smart Citation

Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM

et al. 2022

View full text Add to dashboard Cite

Real-time walking behavior monitoring is essential in ensuring safety and improving people's physical conditions with mobility difficulties. In this paper, a real-time walking motion detection system based on the intelligent walking stick, mobile phone and multi-label imbalance classification method combining focal loss and LightGBM (MFGBoost) is proposed. The Internet of Things (IoT) technology is utilized for communicating between the walking stick and mobile phone. The new MFGBoost is embedded into the Raspberry Pi to classify human motions. MFGBoost is scalable, and other boosting models, such as XGBoost, could also be used as its base classifier. An improved derivation method of the multi-classification focal loss function is proposed in this paper, which is the key to the combination of multi-classification focal loss and Boosting algorithms. We propose a novel denoise method based on window matrix and COPOD algorithm (W-OD). The window matrix is designed to extract data features and smooth noise, and COPOD could output the noise level of the model. A weighted loss function is designed to adjust the model's attention to different samples based on the W-OD algorithm. We evaluate the latest classification model from multiple perspectives on multiple benchmark datasets and demonstrate that MFGBoost and W-OD-MFGBoost could improve classification performance and decision-making efficiency. Experiments conducted on human motion datasets show that W-OD-MFGBoost could achieve more than 97 percent classification accuracy.

show abstract

Section: Human Motion Analysis Of the Intelligent Walking Stickmentioning

confidence: 99%

Section: Human Motion Analysis Of the Intelligent Walking Stickmentioning

confidence: 99%

Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM

et al. 2022

View full text Add to dashboard Cite

show abstract

“…in Japanese. In our previous research [30], we have reported that such an auditory communication has the potential to provide an affinity for the robot. This functionality is useful for the instructors as well to know the robot state without having a look at the above GUI and to concentrate on supporting the user physically during gait trials.…”

Section: User Interfacementioning

confidence: 99%

“…This research was funded in part by the Research Funding for Longevity Sciences (28)(29)(30)(31)(32)(33)(34) form the National Center for Geriatrics and Gerontology (NCGG).…”

Section: Acknowlegementsmentioning

confidence: 99%

“…In this paper, we propose a touchable robotic device accompanying the user automatically and supporting the user's weight in case of emergency, and investigate the feasibility of the robot in terms of the touching effect based postural stabilization through a clinical pilot experiment. Previously, our research group has been developing a series of cane-type robots named Intelligent Cane as a mobile hand-holding device [29,30]. With the focus on safe walking, we have introduced design strategies for an admittance control model for fall prevention of the user and robotic gait rehabilitation [31].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A clinical pilot study on posture stabilization via light contact with cane-type companion robot

et al. 2019

Self Cite

View full text Add to dashboard Cite

In this paper, toward robotic gait assistance, we investigate the feasibility of a cane-type assistive mobile robot accompanying the user autonomously through a clinical pilot experiment. As widely known, gait ability is important for all people to keep their quality of life. However, for people having weakened lower limbs such as elderly people, their postural sway during walking could be insuppressible and cause falling. To support the gait motion of elderly people, our group has been developing a series of hand-holding cane robots named Intelligent Cane. Such assistive robots are expected to remove barriers to the independent lives of elderly people. Recently, we have focused on the potential of a companion robot that follows the user ahead and can be touched or grasped for bracing whenever the user needs it. In order to demonstrate proof of our concept through an experiment with a motion capture system, we propose a user companion strategy that enables our cane robot to keep a constant relative distance between the robot and the user walking on a treadmill. We evaluate the accuracy of the user companion in an experiment where a user walks on a treadmill. Then, we conduct a clinical experiment with three healthy subjects walking on with the treadmill with our cane robot as a pilot study. Through the clinical experiment, we evaluate a postural stabilization effect of physical interaction with the robot and discuss the feasibility of our robotic gait assistance methodology.

show abstract

Towards physical interaction-based sequential mobility assistance using latent generative model of movement state

et al. 2020

View full text Add to dashboard Cite

Adaptive walking load control for training physical strength using cane-type robot

Cited by 7 publications

References 10 publications

Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM

Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM

A clinical pilot study on posture stabilization via light contact with cane-type companion robot

Towards physical interaction-based sequential mobility assistance using latent generative model of movement state

Contact Info

Product

Resources

About