Darwin Gouwanda

A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits.

Gait events detection allows clinicians and biomechanics researchers to determine timing of gait events, to estimate duration of stance phase and swing phase and to segment gait data. It also aids biomedical engineers to improve the design of orthoses and FES (functional electrical stimulation) systems. In recent years, researchers have resorted to using gyroscopes to determine heel-strike (HS) and toe-off (TO) events in gait cycles. However, these methods are subjected to significant delays when implemented in real-time gait monitoring devices, orthoses, and FES systems. Therefore, the work presented in this paper proposes a method that addresses these delays, to ensure real-time gait event detection. The proposed algorithm combines the use of heuristics and zero-crossing method to identify HS and TO. Experiments involving: (1) normal walking; (2) walking with knee brace; and (3) walking with ankle brace for overground walking and treadmill walking were designed to verify and validate the identified HS and TO. The performance of the proposed method was compared against the established gait detection algorithms. It was observed that the proposed method produced detection rate that was comparable to earlier reported methods and recorded reduced time delays, at an average of 100 ms.

Optimization of Wastewater Anaerobic Digestion Using Mechanistic and Meta-heuristic Methods: Current Limitations and Future Opportunities

Anaerobic digestion is widely used to treat high-strength wastewater and produces methane as a by-product for power generation. Treatment and reuse of industrial effluent also contribute to water conservation efforts. Nevertheless, the sensitivity of anaerobic digestion system proves to be a challenge in ensuring consistent quality of treated wastewater and biogas production. Hence, it is essential to devise an effective model and control system that accurately represent the dynamics of anaerobic digestion and can respond to changes in process parameters with proper fault detection and output prediction. This article provides a comprehensive review on (1) the anaerobic digester technology and parameters governing its efficiency, (2) mechanistic and meta-heuristic models used to describe this process, and (3) the process control strategies. In this study, adaptive controller was found to be able to provide wider options in terms of controlled and manipulated variables. Nevertheless, an in-depth study is essential to determine the best controller to be applied for a particular system where further optimization can be done to achieve the best performance.

A review on the mechanical design elements of ankle rehabilitation robot

Ankle rehabilitation robots are developed to enhance ankle strength, flexibility and proprioception after injury and to promote motor learning and ankle plasticity in patients with drop foot. This article reviews the design elements that have been incorporated into the existing robots, for example, backdrivability, safety measures and type of actuation. It also discusses numerous challenges faced by engineers in designing this robot, including robot stability and its dynamic characteristics, universal evaluation criteria to assess end-user comfort, safety and training performance and the scientific basis on the optimal rehabilitation strategies to improve ankle condition. This article can serve as a reference to design robot with better stability and dynamic characteristics and good safety measures against internal and external events. It can also serve as a guideline for the engineers to report their designs and findings.

A Low Cost Alternative to Monitor Human Gait Temporal Parameters–Wearable Wireless Gyroscope

Gait events and temporal parameters are presently identified by requiring both patient and healthcare professional to be present at a gait laboratory. This requirement is not feasible for the general population in developing countries, where access to such laboratories is limited and costly. Therefore, an inexpensive gait monitoring device has great potential to address this limitation. This paper presents the development of a low cost wearable wireless device that relies on gyroscopic data to: 1) identify gait events, that is, heel-strike (HS) and toe-off (TO), and 2) determine gait temporal parameters. This wearable device is to be worn around the human foot and ankle. It relies on heuristics and zero-crossing method to detect HS and TO accurately. In the experimental study, the identified gait events were benchmarked against force plate. The average errors were found to be between -20.45 and -17.33 ms for HS and -25.21 and -8.97 ms for TO. The R2 and Bland Altman plot showed positive and consistent results. These findings demonstrated the viability of the proposed system in identifying gait events and determining gait temporal parameters.

Development of an intelligent real-time heuristic-based algorithm to identify human gait events

Human gait events are crucial in gait analysis. Proper identification of gait events enables derivation of various spatio-temporal parameters and segmentation of the data captured by motion capture systems. Rapid growth of miniature motion sensors, such as gyroscope, has allowed human motion to be captured continuously in real-time and in natural environment. This paper utilizes gyroscopes to monitor human gait and heuristic-based algorithm to define initial contact (IC) and end contact (EC) of the foot on the ground during walking. Experimental study was conducted to examine the overall performance of the proposed method. To simulate abnormal gait, knee brace and ankle brace were used to restrict knee and ankle movements during walking. This study showed that the heuristic-based algorithm can continuously define IC and EC accurately in normal and abnormal gaits.

Investigating running gait using a shoe-integrated wireless inertial sensor

Inertial sensor have been widely used to analyze human walking, but only few studies used inertial sensor to analyze running gait. This paper presented the use of inertial sensor to identify running gait phases and to estimate running speed. An experimental study were conducted to validate the feasibility of this approach. In this experiment, sensor were positioned on top of the third metatarsal of the foot. Subjects were requested to run at 8 km/h - 11 km/h. Raw acceleration and angular velocity were recorded. An off-phase segmentation was then employed to segment the running sequence into individual stride. The orientation of the foot was determined through integration and the running speed was estimated by integrating the acceleration data. The results were satisfactory. The root mean square error (RMSE) were found to be ranging between 4.54% and 5.59% for all the estimated running speed.

Fuzzy logic modelling for thermophilic anaerobic digestion of palm oil mill effluent (POME) treatment

Treatment of POME has become more challenging with stringent effluent discharge standards and difficulty of current treatment processes to meet the standards due to the complex nature of POME. Utilization of high-rate anaerobic reactor operated under thermophilic condition is proposed for primary POME treatment due to low energy requirements and biogas production that could generate revenue to the industry. However, successful implementation of such system is dependent on the availability of a well-defined model. Hence, fuzzy inference method was employed to model the thermophilic anaerobic digestion of POME. Fuzzy Inference Model (FIM) which utilizes historical data of the reactor condition and digestion performance showed worthy estimation, with an average error and standard deviation of 2.81% and 2.10%; 10.35% and 8.67%; 12.22% and 10.40% for pH, COD and TSS correspondingly.

Wireless ambulatory monitoring system with online periodical gait assessment

This paper presents a wireless gait-monitoring system which periodically examine human gait. It introduces two new methods. The first method is an algorithm that integrates the Multi-Resolution Wavelet Decomposition (MWD) with peak-valley detection algorithm to identify the occurrences of heel-strike and toe-off. The second method is the Coefficient of Determination (CoD). CoD provides a single-value indicator that defines gait normality. In the experimental study, abnormal gait is simulated by placing a load on one limb. Significant differences were found in the temporal parameters, and the orientation of the lower extremity ( p < 0.01). These experiments demonstrated the capability of the system in periodically evaluating human gait.

Estimation of vertical ground reaction force during running using neural network model and uniaxial accelerometer

Wearable technology has been viewed as one of the plausible alternatives to capture human motion in an unconstrained environment, especially during running. However, existing methods require kinematic and kinetic measurements of human body segments and can be complicated. This paper investigates the use of neural network model (NN) and accelerometer to estimate vertical ground reaction force (VGRF). An experimental study was conducted to collect sufficient samples for training, validation and testing. The estimated results were compared with VGRF measured using an instrumented treadmill. The estimates yielded an average root mean square error of less than 0.017 of the body weight (BW) and a cross-correlation coefficient greater than 0.99. The results also demonstrated that NN could estimate impact force and active force with average errors ranging between 0.10 and 0.18 of BW at different running speeds. Using NN and uniaxial accelerometer can (1) simplify the estimation of VGRF, (2) reduce the computational requirement and (3) reduce the necessity of multiple wearable sensors to obtain relevant parameters.

Moving toward Soft Robotics: A Decade Review of the Design of Hand Exoskeletons

Soft robotics is a branch of robotics that deals with mechatronics and electromechanical systems primarily made of soft materials. This paper presents a summary of a chronicle study of various soft robotic hand exoskeletons, with different electroencephalography (EEG)- and electromyography (EMG)-based instrumentations and controls, for rehabilitation and assistance in activities of daily living. A total of 45 soft robotic hand exoskeletons are reviewed. The study follows two methodological frameworks: a systematic review and a chronological review of the exoskeletons. The first approach summarizes the designs of different soft robotic hand exoskeletons based on their mechanical, electrical and functional attributes, including the degree of freedom, number of fingers, force transmission, actuation mode and control strategy. The second approach discusses the technological trend of soft robotic hand exoskeletons in the past decade. The timeline analysis demonstrates the transformation of the exoskeletons from rigid ferrous materials to soft elastomeric materials. It uncovers recent research, development and integration of their mechanical and electrical components. It also approximates the future of the soft robotic hand exoskeletons and some of their crucial design attributes.