Amputee Person Research Articles

Pressure and Angle Sensors with Optical Fiber for Instrumentation of the PrHand Hand Prosthesis

The principal cause of upper limb amputations is due to traumatism. The prosthesis is an assistive device to help in the activities of daily for the amputee person. However, one of the latest reports shows that in developing countries there are around 30 million people without assistive devices. This work presents the development of two kinds of sensors for the PrHand, an upper limb prosthesis based on compliant mechanism and soft-robotics. The sensors are made with polymeric optical fiber (POF), due to their flexibility and low cost, and the working principle is based on intensity variation. The angle sensors are used for monitoring the interphalangeal joint of the fingers, and for the assessment were made cycles of closing and opening each finger. On the other hand, the force sensors are located at the tip of three fingers to track the force made over the objects. Before encoring the sensors were evaluated making five cycles of compressing and decompressing each sensor. The results show a linear behavior between the angle and the voltage variation, one most remarkable angle sensor result was with a sensibility of 0.0357 V/° and an R2 of 99 % closing and 0.0483 V/° opening. In the case of the force sensor, a polynomial relation was found between the voltage changes and the pressure over the sensor; in some cases, the relation between voltage changes and pressure could be linear but that depends on the construction of the sensor. Regarding the obtained R2 of 99 %, its sensibility was 0.0361 V/N compression and 0.0368 V/N decompression. In conclusion, was successfully developed two kinds of sensors for the instrumentation of PrHand prosthesis. It is expected to use angle and sensor variables as input in algorithms of Machine Learning to improve the detection of objects. One aspect to improve is to control in a better way the sensor construction parameters due to the considerable influence over the sensor behavior.

Investigation of the Number of Features and Muscles for an Effective Hand Motion Classifier Using Electromyography Signal

The essential problem in the development of a prosthetic hand based on electromyography (EMG) signal is the choice of the features and number of muscles that will be used to recognize the hand motion. A minimal number of feature and channel could reduce the cost in the development of the prosthetic hand and processing time. Therefore, it is important to obtain the correct number of feature and muscles in the system. The objective of this study is to evaluate the effectiveness of using the number of feature and muscle to recognize the hand motion for amputee person using the EMG signal. In this study, the EMG dataset was obtained from five transradial amputee persons. Ten disposable electrodes (Ag/AgCl) were placed on the residual hand with equal space between the electrodes. In order to obtain the EMG features which are related to the hand motion, each of the EMG signal was extracted using six-time domain features which are root mean square (RMS), integrated EMG (IEMG), waveform length (WL), difference absolute standard deviation value (DASDV), Wilson amplitude, and myopulse rate (MYOP). Each feature was evaluated using Decision Tree, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-nearest neighborhood, and Ensemble machine learning (ML). The number of class to be classified in this study was eighteen motions. The effectiveness of using the number of features and muscles was evaluated by varying the number of features and muscles. Further, the accuracy, in the discrimination of the motion, was calculated and compared among the machine learnings. The results of this study show that six muscles can effectively classify the eighteen of hand motion. The ML with WL feature has the highest accuracy among the others (81.3% based on quadratic SVM). The study suggested the effective number of muscle and feature which can be used in the prosthetics hand development so that the effective prosthetic machine can be built with a low cost in the time computing and the hardware for data acquisition.

EMG Signal Based Pattern Recognition of Grasping Movement Using MODWT And Weighted K- Nearest Neighbor

This study purposed and evaluates a method based on weighted K-NN classification of surface Electromyogram (sEMG) signals. The sEMG signal classification plays the key role in designing a prosthetic for amputee persons. Wavelet transform is new signal processing technique, which provides better resolution in time and frequency domain simultaneously. Due to these wavelet properties, it can be effectively used in processing the sEMG signal to determine certain amplitude changes at certain frequencies. This paper propose a Maximal overlap Discrete Wavelet Transform (MODWT) approach for Weighted K-NN classifier for classification of sEMG signals based Grasping movements. At level 5 signal decomposition using MODWT, useful resolution component of the sEMG signal is obtained. In this paper Time-domain (TD) features set is used, which shows a decent performance. In WKNN, use a square-inverse weighted technique to improve the performance of the K-NN. Hence, a novel feature set obtained from decomposed signal using MODWT is used to improve the performance of sEMG for classification. MODWT was used for de-noising and time scale feature extraction of sEMG signals. Several WKNN classifiers are tested to optimize classification accuracy and computational problems. PCA is use to reduce the size of the level 5 decomposed data. WKNN performance evaluation on K=10 values with or without PCA. Six hand grasping movements have been classified, results indicate that this method allows the classification of hand pattern recognition with high precision.

Electromyography (EMG) based Classification of Finger Movements using SVM

Myoelectric control prostheses hand are currently popular developing clinical option that offers amputee person to control their artificial hand by analyzing the contacting muscle residual. Myoelectric control system contains three main phase which are data segmentation, feature extraction and classification. The main factor that affect the performance of myoelectric control system is the choice of feature extraction methods. There are two types of feature extraction technique used to extract the signal which are the Hudgins feature consist of Zero Crossing, Waveform Length (WL), Sign Scope Change (SSC) and Mean Absolute Value (MAV), the single Root Mean Square (RMS). Then, the combination of both is proposed in this study. An analysis of these different techniques result were examine to achieve a favorable classification accuracy (CA). Our outcomes demonstrate that the combination of RMS and Hudgins feature set demonstrate the best average classification accuracy for all ten fingers developments. The classification process implemented in this studies is using Support Vector Machine (SVM) technique.

Surface Electromyography Signal Collecting Strategy for Artificial Hand Controlling Purposes

Surface electromyography technique is presented for muscle activation detection and intuitive control of prostheses or robot arm. This research aims to develop a strategy for collecting EMG signal in order to be used as input control for an artificial hand. Methodology of this research composed of, collecting EMG signal using clinical device to choose the best position. Collect the signal using myoware muscle sensor depending on these positions locating above with determining the threshold value for each position. The test was done on a healthy female person and an amputee person, a number of three position were used by the clinical myotrace 400 each positon composed of four channels i, four channel location were chosen from the three position that were used by the myoware sensor on the forearm in addition to two locations on the ulna, while the amputee included only two locations on the ulna. The locations obtained from the myotrace targeting muscles ( extensor carpi ulnaris, flexor digitorium sublimis, brachioradialis, flexor carpi ulnaris). The threshold values obtained were on a range of 150-320 that consider a non-sensitive. For the amputee the threshold values were the from 150-220 for both the healthy arm and the amputee arm which consider a weaker values.

Surface Electromyography Signal Collecting Strategy for Artificial Hand Controlling Purposes

Surface electromyography technique is presented for muscle activation detection and intuitive control of prostheses or robot arm. This research aims to develop a strategy for collecting EMG signal in order to be used as input control for an artificial hand. Methodology of this research composed of, collecting EMG signal using clinical device to choose the best position. Collect the signal using myoware muscle sensor depending on these positions locating above with determining the threshold value for each position. The test was done on a healthy female person and an amputee person, a number of three position were used by the clinical myotrace 400 each positon composed of four channels i, four channel location were chosen from the three position that were used by the myoware sensor on the forearm in addition to two locations on the ulna, while the amputee included only two locations on the ulna. The locations obtained from the myotrace targeting muscles ( extensor carpi ulnaris, flexor digitorium sublimis, brachioradialis, flexor carpi ulnaris). The threshold values obtained were on a range of 150-320 that consider a non-sensitive. For the amputee the threshold values were the from 150-220 for both the healthy arm and the amputee arm which consider a weaker values.

Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals

To perform basic hand movements, a hand amputee person needs an exoskeleton prosthetic hand (EPH). The EPH can be controlled through electroencephalogram (EEG) or electromyogram (EMG) signals. The EMG signals are preferred as they are acquired from surface of forearm and termed as surface EMG (sEMG). It is very challenging to design the control section for EPH. It should be able to classify different hand movements accurately based on the acquired sEMG signals. Also the sEMG signals must be acquired from minimum number of electrodes to make EPH cost-effective. In this paper, we have proposed a novel technique to classify the basic hand movements. The method proposed in this paper applies tunable-Q wavelet transform (TQWT) based filter-bank (TQWT-FB) for decomposition of cross-covariance of sEMG (csEMG) signals. Then, Kraskov entropy (KRE) features are extracted and ranked. The proposed method is tested on the data obtained from five subjects and achieved the average classification accuracy (CA) of 98.55% using k-nearest neighbour (k-NN) classifier. Therefore, our developed prototype is available for further validation using larger diverse data.

Upper body accelerations during level walking in transtibial amputees.

The observation of upper body movement is gaining interest in the gait analysis community. Recent studies involved the use of body-worn motion sensors, allowing translation of laboratory measurements to real-life settings in the context of patient monitoring and fall prevention. It was shown that amputee persons demonstrate altered acceleration patterns due to the presence of prosthetic components, while no information is available on how accelerations propagate upwards to the head during level walking. This descriptive study aims to fill this gap. Original research report. Twenty definitive prosthesis users with transtibial amputation and 20 age-matched able-bodied individuals participated in the study. Three magneto-inertial measurement units were placed at head, sternum and pelvis level to assess acceleration root mean square. Three repetitions of the 10-m walking test were performed at a self-selected speed. Acceleration root mean square was significantly larger at pelvis and head level in individuals with amputation than in able-bodied participants, mainly in the transverse plane ( p < 0.05). Differences were also observed in how accelerations propagate upwards, highlighting that a different motor strategy is adopted in amputee persons gait to compensate for increased instability. The obtained parameters allow an objective mobility assessment of amputee persons that can integrate with the traditional clinical approach. Transtibial amputees exhibit asymmetries due to the sound limb's support prevalence during gait: this is evidenced by amplified accelerations on the transverse plane and by related differences in upper body movement control. Assessing these accelerations and their attenuations upwards may be helpful to understand amputee's motor strategies and to improve prosthetic training.

Rehabilitation strategies after 2004 Aceh earthquake and tsunami disaster in Indonesia: Case report

December 26, 2004 an earthquake with a magnitude of 8,9 SR US followed by tsunami has hit the west and north coast of Nangroe Aceh Darussalam (NAD) province and North Sumatra region in Indonesia. Hundreds of thousands has been dead, many more have been injured, it also caused damaged to almost every infrastructures including health services. Health problem related to earthquake and tsunami other than respiratory problems are multiple bone fractures, peripheral nerve injuries, severe infected wounds, gangrenes that often lead to amputation of the injured limbs. The only rehabilitation centre in Aceh province at Zainoel Abidin hospital in Banda Aceh, have been damaged seriously including all equipments for mobility and the prosthesis-orthosis workshop. Many limb amputee persons, has great need for prosthesis so they can walk and do their daily living activity. Prosthesis manufactures need special skill and are individually design. After early assessment, a special rehabilitation medicine team together with an international NGO, work for relief after disaster, made and distribute free prosthetic-orthotics. From April 24, 2005 till March 29, 2006 the rehabilitation medicine team and prosthetic-orthotic technicians has made seven visits to Banda Aceh and approximately for three to four days on each visit. The team made and give one hundred fifty (150) free prosthesis-orthosis appliances for 134 amputee. All these prosthetic-orthotic appliances was made at the orthotic-prosthetic laboratory. Department of Rehabilitation Medicine, Cipto Mangunkusumo teaching hospitals in Jakarta. There is a great need to rebuilt rehabilitation medicine services in Aceh with its infrastructures, equipments and specialized trained person such as rehabilitation medicine specialist and prosthetic-orthotic technician. Long-term plan from rehabilitation medicine view are to develop a mobile limb workshop for rehabilitation medicine services, so an outreach program can be done more effectively.

Patient education after amputation: Systematic review and experts’ opinions

ObjectiveTo perform a systematic review of the literature regarding amputee self-care, and analyze current experts’ opinions. MethodThe research in Medline and Cochrane Library databases was performed using the keywords “amputee self-care”, “amputee health care”, “amputee education”, and “amputee health management”. The methodological quality of the articles was assessed using four levels of evidence and three guideline grades (A: strong; B: moderate; C: poor). ResultOne prospective randomized controlled study confirm the level of evidence of self-care amputee persons with grade B, which is similar others chronic diseases self-care. Self-care of amputee persons contributes to improve functional status, depressive syndrome, and also health-related quality of life. A review of the patients’ needs and expectations in self-care amputee persons has been established thanks to the presence of qualitative focus group study. ConclusionA multidisciplinary self-care of amputee persons can be recommended. Regarding literature date, the level of evidence of self-care amputee persons is moderate (grade B). Experts groups are currently working on a self-care amputee persons guideline book in order to standardize practicing and programs in the physical medicine and rehabilitation departments.

Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography

A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement.

A design approach for myoelectric arm with hand and wrist motions using single actuator

This paper relates to the development of prosthetic myoelectric hand that performs many functions of real human hand like opening and closing of fingers and wrist rotation. This dual movement of prosthetic hand is controlled by a single DC motor. Below-elbow amputee persons with missing limbs can append this prosthetic hand with the available stump and can do some of the hand operations with multiple degrees of freedom by voluntary activation of muscles using electromyogram (EMG) electrodes. To rehabilitate such a person, facilities like opening, closing, grasping, and lifting objects of different weight with variable grip force are available like natural hand. The main design consideration includes degrees of freedom, number of actuators, power of actuators, reliability, electronic control, light weight, economic viability, wrist rotation, variable grip force pattern, and ease of attachment with limb. Use of electromagnetic clutches along with the coupling logic of DC motor and microcontroller based on grip force generation and wrist rotation based on EMG signals imparts a new function to the device. It will be useful for both robotic and prosthetic industry.