Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics

Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed

Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors

Recognition of movement is a crucial process to assist humans in activities of daily living, such as walking. In this work, a high-level method for the simultaneous recognition of locomotion and gait phases using wearable sensors is presented. A Bayesian formulation is employed to iteratively accumulate evidence to reduce uncertainty, and to improve the recognition accuracy. This process uses a sequential analysis method to autonomously make decisions, whenever the recognition system perceives that there is enough evidence accumulated. We use data from three wearable sensors, attached to the thigh, shank, and foot of healthy humans. Level-ground walking, ramp ascent and descent activities are used for data collection and recognition. In addition, an approach for segmentation of the gait cycle for recognition of stance and swing phases is presented. Validation results show that the simultaneous Bayesian recognition method is capable to recognize walking activities and gait phases with mean accuracies of 99.87% and 99.20%. This process requires a mean of 25 and 13 sensor samples to make a decision for locomotion mode and gait phases, respectively. The recognition process is analyzed using different levels of confidence to show that our method is highly accurate, fast, and adaptable to specific requirements of accuracy and speed. Overall, the simultaneous Bayesian recognition method demonstrates its benefits for recognition using wearable sensors, which can be employed to provide reliable assistance to humans in their walking activities

Probabilistic locomotion mode recognition with wearable sensors

Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to assist humans in walking activities. We present a probabilistic Bayesian approach with a sequential analysis method for recognition of locomotion and phases of the gait cycle. Our approach uses recursive accumulation of evidence, as biological systems do, to reduce uncertainty present in the sensor measurements, and thus improving recognition accuracy. Data were collected from a wearable sensor, attached to the shank of healthy human participants, from three locomotion modes; level-ground walking, ramp ascent and ramp descent. We validated our probabilistic approach with recognition of locomotion in steady-state and gait phases in transitional states. Furthermore, we evaluated the effect, in recognition accuracy, of the accumulation of evidence controlled by increasing belief thresholds. High accuracy results achieved by our approach, demonstrate its potential for robust control of lower limb wearable soft robotic devices to provide natural and safe walking assistance to humans

Prediction of gait events in walking activities with a Bayesian perception system

In this paper, a robust probabilistic formulation for prediction of gait events from human walking activities using wearable sensors is presented. This approach combines the output from a Bayesian perception system with observations from actions and decisions made over time. The perception system makes decisions about the current gait events, while observations from decisions and actions allow to predict the most probable gait event during walking activities. Furthermore, our proposed method is capable to evaluate the accuracy of its predictions, which permits to obtain a better performance and trade-off between accuracy and speed. In our work, we use data from wearable inertial measurement sensors attached to the thigh, shank and foot of human participants. The proposed perception system is validated with multiple experiments for recognition and prediction of gait events using angular velocity data from three walking activities; level-ground, ramp ascent and ramp descent. The results show that our method is fast, accurate and capable to evaluate and adapt its own performance. Overall, our Bayesian perception system demonstrates to be a suitable high-level method for the development of reliable and intelligent assistive and rehabilitation robots

A broadband multi-distance approach to measure tissue oxygen saturation with continuous wave near-infrared spectroscopy

Brain tissue oxygen saturation, StO2, measured with near-infrared spectroscopy (NIRS) is of great clinical interest as it quantifies the balance between cerebral oxygen supply and demand. Some brain oximeters are based on spatially resolved spectroscopy (SRS), where NIRS data is collected at multiple distances from the light source to estimate a slope of light attenuation against distance. Other use a broadband approach which utilizes derivatives of the absorption spectra to estimate StO2, such as broadband fitting (BF). We describe a novel algorithm, broadband spatially resolved spectroscopy (BB-SRS), for estimating StO2. It is based on comparing the measured slope to a model of the attenuation slope, which depends on the optical properties of tissue. Fitting this model with a least squares fitting procedure recovers parameters describing absorption and scattering; the concentrations of oxy- and deoxy-haemoglobin and hence StO2 and the scattering parameters β and α describing the exponential dependence of scattering on wavelength. To demonstrate BB-SRS, a broadband spectrum (700 - 1000 nm, step size 2 nm) was simulated in NIRFAST and was analysed with BB-SRS, SRS and BF. The developed BB-SRS algorithm recovered StO2 with a relative error of -9%; the concentration of deoxyhaemoglobin with a relative error of +4% , oxyhaemoglobin -10%. The scattering parameters β and α were recovered with a relative error of -30% and -2%, respectively. Among the three algorithms, BB-SRS performed with the best relative error

Exponential corrected thermodynamics of Born-Infeld BTZ black holes in massive gravity

It is known that entropy of black hole gets correction at quantum level. Universally, these corrections are logarithmic and exponential in nature. We analyze the impacts of these quantum corrections on thermodynamics of Born-Infeld BTZ black hole in massive gravity by considering both such kinds of correction. We do comparative analysis of corrected thermodynamics with their equilibrium values. Here, we find that the exponential correction yields to the second point of the first order phase transition. Also, quantum correction effects significantly on the Helmholtz free energy of larger black holes. We study the equation of state for the exponential corrected black hole to obtain a leading order virial expansion.Comment: 16 pages, 4 figure

Portable haptic device for lower limb amputee gait feedback: assessing static and dynamic perceptibility

Loss of joints and severed sensory pathway cause reduced mobility capabilities in lower limb amputees. Although prosthetic devices attempt to restore normal mobility functions, lack of awareness and control of limb placement increase the risk of falling and causing amputee to have high level of visual dependency. Haptic feedback can serve as a cue for gait events during ambulation thus providing sense of awareness of the limb position. This paper presents a wireless wearable skin stretch haptic device to be fitted around the thigh region. The movement profile of the device was characterized and a preliminary work with able-bodied participants and an above-knee amputee to assess the ability of users to perceive the delivered stimuli during static and dynamic mode is reported. Perceptibility was found to be increasing with stretch magnitude. It was observed that a higher magnitude of stretch was needed for the stimuli to be accurately perceived during walking in comparison to static standing, most likely due to the intense movement of the muscle and increased motor skills demand during walking activity

Accelerated Expansion of the Universe in Gauss-Bonnet Gravity

We show that in Gauss-Bonnet gravity with negative Gauss-Bonnet coefficient and without a cosmological constant, one can explain the acceleration of the expanding Universe. We first introduce a solution of the Gauss-Bonnet gravity with negative Gauss-Bonnet coefficient and no cosmological constant term in an empty $(n+1)$-dimensional bulk. This solution can generate a de Sitter spacetime with curvature $n(n+1)/\{(n-2)(n-3)|\alpha|\}$. We show that an $(n-1)$-dimensional brane embedded in this bulk can have an expanding feature with acceleration. We also considered a 4-dimensional brane world in a 5-dimensional empty space with zero cosmological constant and obtain the modified Friedmann equations. The solution of these modified equations in matter-dominated era presents an expanding Universe with negative deceleration and positive jerk which is consistent with the recent cosmological data. We also find that for this solution, the $"n"th$ derivative of the scale factor with respect to time can be expressed only in terms of Hubble and deceleration parameters.Comment: 12 pages, no figure, references added, typos corrected, Section 4 ammended, an appndix added, version to be appeared in Phys. Rev.

Kerr de Sitter Spacetimes in Various Dimension and dS/CFT Correspondence

We consider the Kerr-de Sitter (Kerr-dS) black hole in various dimensions. Introducing a counterterm, we show that the total action of these spacetimes are finite. We compute the masses and the angular momenta of Kerr-dS spaces with one rotational parameter in four, five and seven dimensions. These conserved charges are also computed for the case of Kerr-dS space with two rotational parameters in five dimensions. Although the angular momentum density due to the counterterm is nonzero, it gives a vanishing contribution to the total angular momentum. We also find that the total angular momentum of the spacetime is independent of the radius of the boundary for all cases, a fact that is not true for the total mass of the system.Comment: 11 pages, no figure, reference added, the version to be published in Phys. Rev. D6

Mass and angular momenta of Kerr anti-de Sitter spacetimes in Einstein-Gauss-Bonnet theory

We compute the mass and angular momenta of rotating anti-de Sitter spacetimes in Einstein-Gauss-Bonnet theory of gravity using a superpotential derived from standard Noether identities. The calculation relies on the fact that the Einstein and Einstein-Gauss-Bonnet vacuum equations are the same when linearized on maximally symmetric backgrounds and uses the recently discovered D-dimensional Kerr-anti-de Sitter solutions to Einstein's equations