Hidden markov modeling of human pathological gait using laser range finder for an assisted living intelligent robotic walker

Papageorgiou, Xanthi S.; Chalvatzaki, Georgia; Tzafestas, Costas S.; Maragos, Petros

doi:10.1109/iros.2015.7354283

Cited by 17 publications

(7 citation statements)

References 22 publications

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“…As an example, ref. [99] uses a laser range finder sensor to detect and track the human legs in order to recognize gait patterns. In [99], an adapted Hidden Markov Model (HMM) was developed to obtain an appropriate state estimation of human walk.…”

Section: Robot Learningmentioning

confidence: 99%

“…[99] uses a laser range finder sensor to detect and track the human legs in order to recognize gait patterns. In [99], an adapted Hidden Markov Model (HMM) was developed to obtain an appropriate state estimation of human walk. Another example is presented in [100] where the HMM was used to estimate human affective state in real time by collecting data of heart rate, perspiration rate, and facial muscle contraction from several humans.…”

Section: Robot Learningmentioning

confidence: 99%

“…As mentioned previously, existing research includes context-aware object recognition [69], gesture recognition [17], human patterns recognition [99], human action prediction [98], learning manipulation skills from human demonstrations [103], and fine-tuning learned skills by exploring efficient forms of reinforcement learning [106]. Machine learning methods are increasingly used in robotic applications due to advances in their mathematical formalization.…”

Section: Limitations and Opportunities For Cognitive Collaborationmentioning

confidence: 99%

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Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Castro

Silva

Santos

2021

Sensors

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Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

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Section: Robot Learningmentioning

confidence: 99%

Section: Robot Learningmentioning

confidence: 99%

Section: Limitations and Opportunities For Cognitive Collaborationmentioning

confidence: 99%

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Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Castro

Silva

Santos

2021

Sensors

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show abstract

“…Joint kinematic data has previously been labeled with gait phases using Gaussian Mixture Hidden Markov Models (GHMMs), which infer a series of discrete states from measured data. Previous work using GHMMs to model gait have established that the hidden states have the same duration as the gait phases, both in healthy populations [1] and in populations with pathological gaits [2,3]. However, the correspondence between the GHMM states and gait phase transitions times has not been validated against external measures of gait phase, especially on a step-by-step basis.…”

Section: Introductionmentioning

confidence: 99%

“…GHMM approaches also lack models of gait dynamics necessary for control synthesis and for predicting future states. GHMMs assume the observed kinematics are independent of each other across time and are normally distributed within each phase, creating a static, statistical model of gait kinematics [1,2]. GHMMs can only describe gait in terms of the mean and covariance within each phase; they cannot recreate trajectories of limb motion or simulate the effects of external perturbations on the joint angles.…”

Section: Introductionmentioning

confidence: 99%