Abstract

Physical inactivity is a major national concern, particularly among individuals with chronic conditions and/or disabilities. There is an urgent need to devise practical and innovative fitness methods, designed and grounded in physical, psychological and social considerations that will effectively promote physical fitness participation among individuals of all age groups with chronic health condition(s) and/or disabilities. This research is dedicated to achieving Versatile, Individualized, and Generative ORchestrator (VIGOR) to motivate the movement of the people with limited mobility. Tai-Chi is a traditional mind–body wellness and healing art, and its clinical benefits have been well documented. This work presents a Tai-Chi based VIGOR under development. Through the use of Helping, Pushing and Coaching (HPC) functions by following Tai-Chi kinematics, the VIGOR system is designed to make engagement in physical activity an affordable, individually engaging, and enjoyable experience for individuals who live with mobility due to disease or injury. VIGOR consists of the following major modules: (1) seamless human-machine interaction based on the acquisition, transmission, and reconstruction of 4D data (XYZ plus somatosensory) using affordable I/O instruments such as Kinect, Sensor and Tactile actuator, and active-orthosis/exoskeleton; (2) processing and normalization of kinetic data; (3) Identification and grading of kinetics in real time; (4) adaptive virtual limb generation and its reconstruction on virtual reality (VR) or active-orthosis/exoskeleton; and (5) individualized physical activity choreography (i.e., creative movement design). Aiming at developing a deep-learning-enabled rehab and fitness modality through infusing the domain knowledge (physical therapy, medical anthropology, psychology, electrical engineering, bio-mechanics, and athletic aesthetics) into deep neural network, this work is transformative in that the technology can be applied to the broad research areas of intelligent systems, human-computer interaction, and cyber-physical human systems. The resulting VIGOR has significant potentials as both rehabilitative and fitness modalities and can be adapted to other movement modalities and chronic medical conditions (e.g., yoga and balance exercise; fibromyalgia, multiple sclerosis, Parkinson disease).

Highlights

  • 1.1 MotivationPhysical inactivity, among aging adults and home-bound individuals with chronic conditions and/or disabilities, is a major national concern in the United States [1]

  • The integration of Tai-Chi with four-dimensional (4D: the sensory data includes XY-Z plus a somatosensory signal [35, 36]) virtual-reality technology is both innovative and feasible in that: (1) Complex human movement can be deconstructed into primitive components/modes and deep learning methods [37] can be employed to accurately formulate the spatially and temporally dependent kinetic behavior as well as reconstruct incomplete joint movement or distorted movement caused by chronic health condition(s) [38]; (2) 4D kinetic behavior can be captured and reconstructed through modern sensors, actuators, and virtual reality (VR)/augmented reality (AR) technologies to generate seamless human-machine interaction; (3) Despite having significant storage and computation complexity, real-time kinetic analytics is applicable over a cutting-edge big-data engine and high-performance computing platform

  • Entropy or cross-entropy analysis can be performed for the time-series in the frequency domain which is derived from discrete Fourier transformation (DFT) or discrete wavelet transformation (DWT) [58, 59]

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Summary

Motivation

Among aging adults and home-bound individuals with chronic conditions and/or disabilities, is a major national concern in the United States [1]. There is an urgent need to develop practical innovative exercise methods that engage individuals at all ages, including those with chronic health condition(s) and/or disability, increase regular physical activity levels, and translate to improved health with optimal functional ability and participation. Similar software products in Academia include OpenSim (opensim.stanford.edu) and QuaterNet (Facebook AI Research) Unlike those products, VIGOR integrates Tai-Chi, the traditional mind–body wellness and healing art [26, 27], with a series of data-driven computing technologies that will provide customized restorative physical activities for individuals with a broad range of chronic conditions and functional abilities. Enabled by deep learning technology, the proposed Tai-Chi based VIGOR offers several unique advantages as an individualized, effective, sustainable, and restorative fitness modality for users with movement-based chronic health conditions. The integration of Tai-Chi with four-dimensional (4D: the sensory data includes XY-Z plus a somatosensory signal [35, 36]) virtual-reality technology is both innovative and feasible in that: (1) Complex human movement can be deconstructed into primitive components/modes and deep learning methods [37] can be employed to accurately formulate the spatially and temporally dependent kinetic behavior as well as reconstruct incomplete joint movement or distorted movement caused by chronic health condition(s) [38]; (2) 4D kinetic behavior can be captured and reconstructed through modern sensors, actuators, and VR/AR technologies to generate seamless human-machine interaction; (3) Despite having significant storage and computation complexity, real-time kinetic analytics is applicable over a cutting-edge big-data engine and high-performance computing platform

VIGOR’s infrastructure
Research objectives and function modules of VIGOR
Real-time 4D human-machine interaction
Acquisition and processing of kinematic data The Microsoft
Acquisition of tactile data
Real-time, two-way communication
Deployment of VIGOR on affordable hardware using edge computing
Identification and scoring of user’s kinetic movement
Formulating musculoskeletal kinetic features
Spatial normalization
Recovering occlusion-induced missing data
Normalization of the kinetics of users with limited mobility
Entropy-oriented scoring of human motion
Human motion identification based on machine learning
Reconstruction of 4D instruction/feedback for users
Adaptive virtual limb generation
Pipeline of adaptive virtual limb generation
Multivariate time series-based kinetics generation of Virtual Limbs
Loss function for the Generation of virtual limbs’ kinetics
Correction of generated kinetics using time-series prediction model
Formulating the kinetics of virtual limbs using the measured kinetics of functional body parts
Configuration of network architecture according human anatomy
Example: generating virtual legs based on arm movement using VHNN
52 À 57 μs 4 m 37 s
Construction of virtual limb using active orthosis
Individualized movement choreography
Tai-Chi choreography based on LSTM-RNN
Movement choreography based on visible GAN
Polynomial-based Hessian-free Newton–Raphson optimizer
Findings
Conclusion

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