Rita Patterson

Grip Pressure Measurements during Activities of Daily Life

Research has expanded human-machine communication methods past direct programming and standard hand- held joystick control. Individual force sensors have been used as a simple means of providing environmental information to a robot and research has shown that more advanced sensitive skins can be viable input devices. These touch sensitive surfaces allow for additional modes of interaction between machines in open, undefined environments. These interactions include object detection for navigation and safety but can also be used for recognition of users command gestures by their machine partner. Key to successful implementation of these gestures is the understanding of varied strategies used for communication and interaction and the development of performance limits. Data of dominant hand grip forces was collected using a Tekscan Grip VersaTek Pressure Measurement System during opening of a door. Analysis of data from 10 male and female subjects is presented. The results of qualitative and quantitative analysis of these data show variability in hand configurations between users. Average data over the cohort is reported. These data will be used in future work to provide human metrology constraints and limits for use in simulation and design of new, physical human-robot interaction systems.

Surface EMG and intra-socket force measurement to control a prosthetic device

Surface electromyography (SEMG) has been shown to be a robust and reliable interaction method allowing for basic control of powered prosthetic devices. Research has shown a marked decrease in EMG-classification efficiency throughout activities of daily life due to socket shift and movement and fatigue as well as changes in degree of fit of the socket throughout the subjects lifetime. Users with the most severe levels of amputation require the most complex devices with the greatest number of degrees of freedom. Controlling complex dexterous devices with limited available inputs requires the addition of sensing and interaction modalities. However, the larger the amputation severity, the fewer viable SEMG sites are available as control inputs. Previous work reported the use of intra-socket pressure, as measured during wrist flexion and extension, and has shown that it is possible to control a powered prosthetic device with pressure sensors. In this paper, we present data correlations of SEMG data with intra-socket pressure data. Surface EMG sensors and force sensors were housed within a simulated prosthetic cuff fit to a healthy-limbed subject. EMG and intra-socket force data was collected from inside the cuff as a subject performed pre-defined grip motions with their dominant hand. Data fusion algorithms were explored and allowed a subject to use both intra-socket pressure and SEMG data as control inputs for a powered prosthetic device. This additional input modality allows for an improvement in input classification as well as information regarding socket fit through out activities of daily life.

Concurrent surface electromyography and force myography classification during times of prosthetic socket shift and user fatigue

Objective Surface electromyography has been a long-standing source of signals for control of powered prosthetic devices. By contrast, force myography is a more recent alternative to surface electromyography that has the potential to enhance reliability and avoid operational challenges of surface electromyography during use. In this paper, we report on experiments conducted to assess improvements in classification of surface electromyography signals through the addition of collocated force myography consisting of piezo-resistive sensors. Methods Force sensors detect intrasocket pressure changes upon muscle activation due to changes in muscle volume during activities of daily living. A heterogeneous sensor configuration with four surface electromyography–force myography pairs was investigated as a control input for a powered upper limb prosthetic. Training of two different multilevel neural perceptron networks was employed during classification and trained on data gathered during experiments simulating socket shift and muscle fatigue. Results Results indicate that intrasocket pressure data used in conjunction with surface EMG data can improve classification of human intent and control of a powered prosthetic device compared to traditional, surface electromyography only systems. Significance Additional sensors lead to significantly better signal classification during times of user fatigue, poor socket fit, as well as radial and ulnar wrist deviation. Results from experimentally obtained training data sets are presented.