Applying mechatronics to improve the safety of children in vehicles - what can be done?

Nowadays the media have reported an increasing number of cases where children are accidentally being trapped while their parents and guardians are away attending to other matters. In this paper we discuss the feasibility of applying mechatronics to improve the safety of children in vehicles with the ultimate goal of developing a means for parents, guardians and authorities to be informed if ever there is a child trapped in a vehicle and in need of urgent assistance. We have also presented some preliminary experiments we have carried out for a safety alert system which is currently being developed in our lab

An improved parameterized controller reduction technique via new frequency weighted model reduction formulation

In this paper, an improved parameterized controller reduction technique via a new frequency weighted model reduction formulation is developed for the feedback control of MIMO discrete time systems particularly for non-unity feedback control system configurations which have the controller located in the feedback path. New frequency weights which are a function of a free parameter matrix are derived based on a set of equivalent block diagrams and this leads to a generalized double sided frequency weighted model reduction formulation. Solving this generalized double sided frequency weighted model reduction problem for various values of the free parameter results in obtaining controllers which correspond to each value of the free parameter. It is shown that the proposed formulation has a useful characteristic such that selecting a controller which corresponds to a large value of the free parameter results in obtaining an optimal reduced order controller and using this optimal reduced order controller in a closed loop system results in significant reduction in the infinity norm of the approximation error between the original closed loop system and the closed loop system which uses an optimal reduced order controller (when compared to existing frequency weighted model reduction methods

Generalized Gramian based frequency interval model reduction for unstable systems

Frequency interval controllability and observability gramian matrices are important in order to understand the characteristics of systems which are inherently frequency dependent. Obtaining these frequency interval controllability and observability gramian matrices requires solving a pair of Lyapunov equations. However for certain systems these Lyapunov equations are not solvable. In addition the eigenvalues of the product of the frequency interval controllability and observability gramians may also be complex numbers and therefore these gramians are no applicable to used in the context of model reduction. To overcome these issues, generalized frequency interval controllability and observability gramians are introduced in this paper and the applicability of these generalized gramians to be used in model reduction is demonstrated

A portable myoelectric robotic system for light exercise among bedridden and wheelchair bound individuals

In this study an interactive exercise technique is developed to cater to bedridden and wheelchair bound individuals, in order for them to remain both mentally and physically alert despite their immobility. The sedentary lifestyle common among bedridden and wheelchair bound individuals leads to a variety of problems such as stiff muscles and numbness in various parts of the body. To perform this light exercise, surface electromyogram (EMG) signals are measured from major muscles from the body responsible for human motion. These measured signals are processed and further used to control the movements of a mobile robot (either left, right or straight) wirelessly using radio frequency transmission

Mapping of EMG signal to hand grip force at varying wrist angles

Limb loss is a growing problem in Malaysia and the rest of the world due to the increasing number of industrial accidents, diseases and armed conflicts. After a tragic incident resulting in an amputation or paralysis, the disabled individual needs to be assisted with all possible technological means to improve his quality of life. A cybernetic prosthesis is a device which can greatly assist individuals with hand disabilities by enabling them to have some of the hand capabilities of an able bodied individual. The central nervous system which consists of the brain and spine governs hand grip force and hand movement in the human body by spatial and temporal motor unit recruitments. Electromyogram (EMG) is an electrical biological signal that can be measured from the skin surface and consists of the summation of Motor Unit Action Potentials (MUAP). Hand grip strength, wrist extension and wrist flexion are hand functions which result from the forearm muscle activity and are used in a wide range of daily tasks. Extracting hand grip force and wrist angle information from forearm EMG signals is useful to be used as inputs for the control of cybernetic prostheses. By establishing the relationship between forearm EMG and hand grip force/wrist angles, the prosthetic hand can be controlled in a manner that is customized to an amputee’s intent. In this research work, a myoelectric interface which consists of an electronic conditioning circuit to measure EMG signals and the software to record and process the EMG signals was developed. Experimental training and testing data sets from five subjects were collected to investigate the relationship between forearm EMG, hand grip force and wrist angle simultaneously

Development of EMG circuit to study the relationship between Flexor Digitorum Superficialis muscle activity and hand grip strength

Hand grip strength plays a vital role in performing basic daily tasks such as holding an object. These tasks require a lot of effort from the muscles in the forearm. In this paper, we study the relationship between the muscular effort of the flexor muscles in the forearm and hand grip strength. In order to do that, an electronic circuit was constructed to amplify and filter the electromyogram (EMG) signals measured from the Flexor Digitorum Superficialis (FDS) muscle. The EMG signals measured from the FDS are used to study the relationship between muscular effort of the flexor muscles in the forearm and hand grip strength. EMG signals were measured from the subject while he applied minimum, intermediate and maximum hand grips on a hand gripper. The results show that EMG frequency from the FDS increase with increased hand grip strength. This information relating EMG from flexor muscles to hand grip strength is useful to be used in hand rehabilitation devices to estimate suitable resistance to be provided to patients during rehabilitation routines. Each stage of the circuit development is described in detail so that this experiment can be easily reproduced by others

Development of a myoelectric interface for indirect hand grip force and wrist angle measurement/analysis

Limb loss is a growing problem due to the increasing number of accidents worldwide. A cybernetic prosthesis is a device which can assist individuals with hand disabilities by enabling them to have some of the hand capabilities of an able bodied individual. Extracting hand grip force and wrist angle information from forearm electromyogram (EMG) signals is useful to be used as inputs for the control of cybernetic prostheses. By establishing the relationship between forearm EMG and hand grip force/wrist angles, the prosthetic hand can be controlled in a manner that is customised to an amputee’s intent. In this research work, a myoelectric interface which consists of an electronic conditioning circuit to measure EMG signals and software to record and process the EMG signals were developed. Experimental training and testing datasets from five subjects were collected to investigate the relationship between forearm EMG, hand grip force and wrist angle simultaneously

Measurement system to study the relationship between forearm EMG signals and hand grip force

Hand grip force, wrist flexion and wrist extension are the result of forearm muscle activity. In certain applications such as controlling the movements of a robotic prosthetic hand, information relating wrist joint angles to forearm muscle activity is useful to be used as part of the control algorithm. In this paper, we study the relationship between the muscular activity of forearm muscles and wrist joint angles/position while hand grip force is varied. In order to do that, an electronic circuit was constructed to amplify and filter the electromyogram (EMG) signals measured from the Flexor Carpi Radialis (FCR), Flexor Digitorum Superficialis (FDS) and Extensor Digitorum Communis (EDC). Neural networks were used to model the relationship between EMG signals and wrist joint angle data at different hand grip strength levels. The performances of the networks were indicated by the corresponding Mean Absolute Error values

Model reduction based on limited time interval impulse response gramians

— This paper presents a new limited time interval impulse response Gramians (LTIRG) based model reduction method for single-input single-output (SISO) continuous-time systems. The proposed approach incorporates the time-interval Gramians with impulse response Gramians to preserve the input-output behaviour of the original system for the specified time interval. A numerical example is given to illustrate the proposed approach, and the results are compared with the standard techniques