Control strategies for smart prosthetic hand technology: An overview

Naidu, D. Subbaram; Chen, Cheng-Hung; Perez, Alba; Schoen, Marco P.

doi:10.1109/iembs.2008.4650164

Cited by 38 publications

(20 citation statements)

References 39 publications

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“…Many different avenues of research are being pursued to improve on current commercial devices. One of these is employing complex machine learning algorithms to allow more natural control [3], [4]. This technique works well to classify a variety of postures in a lab setting, but depends on large training sets and few, if any, have been rigorously tested outside of the lab.…”

Section: Introductionmentioning

confidence: 99%

Exploring Teleimpedance and Tactile Feedback for Intuitive Control of the Pisa/IIT SoftHand

Ajoudani

Godfrey

Bianchi

et al. 2014

IEEE Trans. Haptics

107

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This paper proposes a teleimpedance controller with tactile feedback for more intuitive control of the Pisa/IIT SoftHand. With the aim to realize a robust, efficient and low-cost hand prosthesis design, the SoftHand is developed based on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. Due to the built-in flexibility of the hand joints, as the SoftHand grasps, it follows a synergistic path while allowing grasping of objects of various shapes using only a single motor. The DC motor of the hand incorporates a novel teleimpedance control in which the user's postural and stiffness synergy references are tracked in real-time. In addition, for intuitive control of the hand, two tactile interfaces are developed. The first interface (mechanotactile) exploits a disturbance observer which estimates the interaction forces in contact with the grasped object. Estimated interaction forces are then converted and applied to the upper arm of the user via a custom made pressure cuff. The second interface employs vibrotactile feedback based on surface irregularities and acceleration signals and is used to provide the user with information about the surface properties of the object as well as detection of object slippage while grasping. Grasp robustness and intuitiveness of hand control were evaluated in two sets of experiments. Results suggest that incorporating the aforementioned haptic feedback strategies, together with user-driven compliance of the hand, facilitate execution of safe and stable grasps, while suggesting that a low-cost, robust hand employing hardware-based synergies might be a good alternative to traditional myoelectric prostheses.

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Section: Introductionmentioning

confidence: 99%

Exploring Teleimpedance and Tactile Feedback for Intuitive Control of the Pisa/IIT SoftHand

Ajoudani

Godfrey

Bianchi

et al. 2014

IEEE Trans. Haptics

107

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“…Table I provides a comparison of the entropy, RE and the correlation coefficients between actual force along with the individual WH model and fusion algorithm estimated forces for the 18 different subjects. The models 2 M constructed from the sensor data 2 u -which located right on the motor unit -is yielding a better correlation coefficient and a lower RE between the actual force and the WH model estimated force.…”

Section: R Valuesmentioning

confidence: 99%

“…However, to date, there is no prosthetic device available at an affordable cost that mimics the human hand well. There are also no affordable prosthetics on the market with vibrotactile feedback [2]. In order to measure the electrical activity of the skeletal muscle force, one can utilize an invasive, needle electrode based method or a non-invasive, skin surface based technique.…”

Section: Introductionmentioning

confidence: 99%

Computational intelligence based data fusion algorithm for dynamic sEMG and skeletal muscle force modelling

Potluri

Anugolu

Schoen

et al. 2013

2013 6th International Symposium on Resilient Control Systems (ISRCS)

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-In this work, an array of three surface Electrography (sEMG) sensors are used to acquired muscle extension and contraction signals for 18 healthy test subjects. The skeletal muscle force is estimated using the acquired sEMG signals and a Non-linear Wiener Hammerstein model, relating the two signals in a dynamic fashion. The model is obtained from using System Identification (SI) algorithm. The obtained force models for each sensor are fused using a proposed fuzzy logic concept with the intent to improve the force estimation accuracy and resilience to sensor failure or misalignment. For the fuzzy logic inference system, the sEMG entropy, the relative error, and the correlation of the force signals are considered for defining the membership functions. The proposed fusion algorithm yields an average of 92.49% correlation between the actual force and the overall estimated force output. In addition, the proposed fusionbased approach is implemented on a test platform. Experiments indicate an improvement in finger/hand force estimation.

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“…Although the application of optimal control in biomedical engineering has been around since 1980s 5–7, recently, there is a renewed and intense interest in this area 8–10. There are a number applications of optimal control in biomedical sciences, such as blood glucose control in diabetes 11, 12, anesthesia 13, management of thyroid 14, administration of drugs 7, cancer therapy 7, 15–17, circulatory systems 18, human immunodeficiency virus (HIV) 19, prosthetics 20, 21, predator–prey problem 22, 23, and epidemics problem 22, 23.…”

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confidence: 99%