Prosthetic System Research Articles

A Study Case of Short Dental Implants Loading in the All-On-4® System on Fixed Dental Prostheses: A Finite Element Analysis

The biomechanical research about toothless mandible with four dental implants and a prosthetic system research, which shows the influence of the cantilever length of prosthesis on the weakest structural elements and the load changes in peri-implant bone by changing symmetrically position of the dental implants (in relation to each other), diameters and lengths, and orientation (in relation to chewing surfaces), is not discussed widely in the scientific literature. On the other hand, the use of short implants is more common in practice. Some of these implants are only 8mm in length.The odontologists, implantologists and dental technician can use results received by finite element method (FEM) in the daily clinical practice in order to reduce the potential for mechanical / technological and consequent biological complications already at the planning stage of the treatment, and thus determine best outcomes in the treatment of toothless patients.With a unique interpretation of the results obtained by FEM, the paper discusses the results of implants (8 mm in length with 3.5 mm and 4.5 mm diameter) loading, when the cantilevered dental prostheses are loaded in different positions, as well as implant positions are changed.

Implant Restoration Using a New Cementless Screw-Retained Type Prosthetic (TDP) System: Case Series

The biological factors of dental implants have a significant impact on long-term prognosis. In the cement-retained type or screw cement-retained type prosthesis (SCRP) implants, dental luting cement is used between the abutment and the implant fixture. Residual excess cement remaining around the implant and gingiva is one of the major causes of peri-implantitis, which is the most common cause of late implant failure. The TDP implant prosthetic system is a new cementless screw-retained type prosthetic system, which overcomes the limitations of the conventional implant prosthetic systems. Using this system, not only serves to prevent peri-implantitis caused by residual excess cement, but also the risk of screw loosening and fracture is reduced due to the stress distribution through the substructure (link), which is connected to the zirconia crown via frictional force. In this case report, two patients with tooth defects visited our dental hospital for implant treatment. Both patients were treated using the TDP implant prosthetic system. As a result, functional and esthetically excellent implant prostheses were fabricated and delivered to the patients. On the 3-year follow-up examination, both patients showed successful results, respectively.

A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture classification

Objective. Prosthetic systems are used to improve the quality of life of post-amputation patients, and research on surface electromyography (sEMG)-based gesture classification has yielded rich results. Nonetheless, current gesture classification algorithms focus on the same subject, and cross-individual classification studies that overcome physiological factors are relatively scarce, resulting in a high abandonment rate for clinical prosthetic systems. The purpose of this research is to propose an algorithm that can significantly improve the accuracy of gesture classification across individuals. Approach. Eight healthy adults were recruited, and sEMG data of seven daily gestures were recorded. A modified fuzzy granularized logistic regression (FG_LogR) algorithm is proposed for cross-individual gesture classification. Main results. The results show that the average classification accuracy of the four features based on the FG_LogR algorithm is 79.7%, 83.6%, 79.0%, and 86.1%, while the classification accuracy based on the logistic regression algorithm is 76.2%, 79.5%, 71.1%, and 81.3%, the overall accuracy improved ranging from 3.5% to 7.9%. The performance of the FG_LogR algorithm is also superior to the other five classic algorithms, and the average prediction accuracy has increased by more than 5%. Conclusion. The proposed FG_LogR algorithm improves the accuracy of cross-individual gesture recognition by fuzzy and granulating the features, and has the potential for clinical application. Significance. The proposed algorithm in this study is expected to be combined with other feature optimization methods to achieve more precise and intelligent prosthetic control and solve the problems of poor gesture recognition and high abandonment rate of prosthetic systems.

3D Model Construction System for Personalized Rehabilitation Prosthetics Based on Machine Vision

At present, the molding process of domestic prosthetic sockets is still mainly made by hand. According to statistics, the number of various types of disabilities in my country has reached 85.02 million, of which more than 6 million are disabled in the lower limbs. Therefore, the installation of prosthetics has become an important means of compensating for missing limbs. , but the old process has many problems and is complicated and backward, heavy, poor performance, long production process cycle and not environmentally friendly, and the appearance is not beautiful. These problems can no longer meet the needs of patients in the current society. At the same time, during the "13th Five-Year Plan" period, rehabilitation medicine has become a key industry promoted by the country, and the rehabilitation appliance industry has also developed rapidly. With the promotion of policies and technologies, the market value and space of rehabilitation appliances have shown a good form. Therefore, the research of this project In line with the development trend of the times. This work proposes a personalized rehabilitation prosthetic 3D model construction system based on machine vision. In the era of artificial intelligence, the XTOM 3D scanner under machine vision scans the surface contour of the patient's stump to obtain its point cloud information. The first starting point is innovation, providing a data basis for socket design to improve scanning accuracy and find the most suitable socket structure. Finally, according to the individual needs of the user, the neural network is used to extract video features, analyze the most reasonable movement mode of the patient, and achieve the research on the combination of user behavior and product design, so as to analyze the force of the socket in the later stage. The part of the force analysis is innovated again. By adjusting the gravity distribution of the prosthesis, the angle of force, and the angle of the metal connector, the prosthesis printed by the 3D model for the subsequent industrial-grade 3D printer meets the patient's requirements.

Hierarchical strategy for sEMG classification of the hand/wrist gestures and forces of transradial amputees.

The myoelectric control strategy, based on surface electromyographic signals, has long been used for controlling a prosthetic system with multiple degrees of freedom. Several methods classify gestures and force levels but the simultaneous real-time control of hand/wrist gestures and force levels did not yet reach a satisfactory level of effectiveness. In this work, the hierarchical classification approach, already validated on 31 healthy subjects, was adapted for the real-time control of a multi-DoFs prosthetic system on 15 trans-radial amputees. The effectiveness of the hierarchical classification approach was assessed by evaluating both offline and real-time performance using three algorithms: Logistic Regression (LR), Non-linear Logistic Regression (NLR), and Linear Discriminant Analysis (LDA). The results of this study showed the offline performance of amputees was promising and comparable to healthy subjects, with mean F1 scores of over 90% for the "Hand/wrist gestures classifier" and 95% for the force classifiers, implemented with the three algorithms with features extraction (FE). Another significant finding of this study was the feasibility of using the hierarchical classification strategy for real-time applications, due to its ability to provide a response time of 100 ms while maintaining an average online accuracy of above 90%. A possible solution for real-time control of both hand/wrist gestures and force levels is the combined use of the LR algorithm with FE for the "Hand/wrist gestures classifier", and the NLR with FE for the Spherical and Tip force classifiers.

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach.

In recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object - in this case a prosthetic device - into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction. Based on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees. The classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm).

Learning Ergonomic Control in Human–Robot Symbiotic Walking

This article presents an imitation learning strategy for extracting ergonomically safe control policies in physical human–robot interaction scenarios. The presented approach seeks to proactively reduce the risk of injuries and musculoskeletal disorders by anticipating the ergonomic effects of a robot's actions on a human partner, e.g., how the ankle angle of a prosthesis affects future knee torques of the user. To this end, we extend ensemble Bayesian interaction primitives to enable the prediction of latent biomechanical variables. This methodology yields a reactive control strategy, which we evaluate in an assisted walking task with a robotic lower limb prosthesis. Building upon the learned interaction primitives, we also present a model-predictive control (MPC) strategy that actively steers the human–robot interaction toward ergonomic and safe movement regimes. We compare the introduced control strategies and highlight the framework's ability to generate ergonomic, biomechanically safe assistive prosthetic control. A rich analysis of constrained MPC shows a 20× reduction in the effects of large perturbations on prosthetic control system. We empirically demonstrate a 16% reduction in vertical knee reaction forces in real-world jumping experiments utilizing our control methodology and examine other optimal control strategies in simulated walking experiments.

Mechanical testing of transtibial prosthetic sockets: A discussion paper from the American Orthotic and Prosthetic Association Socket Guidance Workgroup.

The advent of novel manufacturing technologies, materials, and socket design concepts could introduce risks to prosthetic limb users, as the existing knowledge base for safe fabrication may not apply. Moreover, although structural test standards exist for mass-produced prosthetic components, they are not applicable to prosthetic sockets. The "AOPA Socket Guidance Workgroup" was formed in 2020 to provide the prosthetic community with evidence-based clinical best practices and methods in the field of prosthetic socket structural analysis. This multidisciplinary expert workgroup undertook a critical analysis of the knowledge gaps regarding the requirements for mechanical testing of lower limb prosthetic sockets. The Workgroup identified knowledge gaps in 4 domains. Domain 1 describes the shape and composition of a mock residual limb, required to support and generate in vivo representative loading within the socket. Domain 2 concerns prosthetic socket coordinate systems and alignment. Domain 3 regards the components and requirements of test specimens. Finally, Domain 4 considers test conditions, loading parameters, and acceptance criteria. This paper describes these knowledge gaps in detail and recommends potential solution approaches based on literature review, group consensus around existing knowledge, or the formation of new study groups to fill each knowledge gap. Our intent is for the recommendations arising from this paper to support the community (e.g., researchers in the clinic, academia, industry, and funders) in addressing these knowledge gaps.

Simultaneous and Proportional Real-Time Myocontrol of Up to Three Degrees of Freedom of the Wrist and Hand.

Here, we introduce a system for intuitive concurrent hand and wrist control, based on a robust feature-extraction protocol and machine-learning. Using the mean absolute value of high-density EMG, we train a ridge-regressor (RR) on only the sustained portions of the single-DOF contractions and leverage the regressor's inherent ability to provide simultaneous multi-DOF estimates. In this way, we robustly capture the amplitude information of the inputs while harnessing the power of the RR to extrapolate otherwise noisy and often overfitted estimations of dynamic portions of movements. The real-time evaluation of the system on 13 able-bodied participants and an amputee shows that almost all single-DOF tasks could be reached (96% success rate), while at the same time users were able to complete most of the two-DOF (62%) and even some of the very challenging three-DOF tasks (37%). To further investigate the translational potential of the approach, we reduced the original 192-channel setup to a 16-channel configuration and the observed performance did not deteriorate. Notably, the amputee performed similarly well to the other participants, according to all considered metrics. This is the first real-time operated myocontrol system that consistently provides intuitive simultaneous and proportional control over 3-DOFs of wrist and hand, relying on only surface EMG signals from the forearm. Focusing on reduced complexity, a real-time test and the inclusion of an amputee in the study demonstrate the translational potential of the control system for future applications in prosthetic control.

Sensor Selection with Composite Features in Identifying User-Intended Poses for Human-Prosthetic Interfaces.

A Human-Prosthetic Interface (HPI) serves to estimate and realise the limb pose intended by the human user, using the information obtained from sensors worn by the user. In recent studies, the HPI maps multi-joint limb poses (i.e. coordinated movement of the body and limbs) to the inputs of multiple sensors. This is in contrast to the conventional methods where each degree of freedom of the powered prosthesis is mapped to the input of one/a pair of sensors. In this approach, it is necessary to systematically select sensors that carry the most information for the intended set of poses, to improve system accuracy and/or minimise the number of sensors, thus the complexity, in the prosthetic system. In this paper, sensor selection process is systematically formulated to maximise the information contained in the input features for a given number of sensors. Most importantly, it accounts for composite features, which are features requiring information from multiple sensors. Such composite features exist and are important in HPIs as we seek to capture coordinated motion involving movements of multiple limb and body segments. A non-convex optimisation problem is formulated which accounts for the constraint introduced by the composite features. A projection matrix is utilised as the optimisation variable to select intended features for evaluation. The problem is solved by the proposed Sensor Selection with Composite Features (SS-CF) algorithm which adapts convex-relaxation techniques. The SS-CF is benchmarked against HPI with expert-selected sensors in the literature and against a greedy heuristic method. The outcome demonstrated the efficacy of the SS-CF algorithm.

Artificial anatomy.

Artificial anatomy.

Surface EMG Statistical and Performance Analysis of Targeted-Muscle-Reinnervated (TMR) Transhumeral Prosthesis Users in Home and Laboratory Settings.

A pattern-recognition (PR)-based myoelectric control system is the trend of future prostheses development. Compared with conventional prosthetic control systems, PR-based control systems provide high dexterity, with many studies achieving >95% accuracy in the last two decades. However, most research studies have been conducted in the laboratory. There is limited research investigating how EMG signals are acquired when users operate PR-based systems in their home and community environments. This study compares the statistical properties of surface electromyography (sEMG) signals used to calibrate prostheses and quantifies the quality of calibration sEMG data through separability indices, repeatability indices, and correlation coefficients in home and laboratory settings. The results demonstrate no significant differences in classification performance between home and laboratory environments in within-calibration classification error (home: 6.33 ± 2.13%, laboratory: 7.57 ± 3.44%). However, between-calibration classification errors (home: 40.61 ± 9.19%, laboratory: 44.98 ± 12.15%) were statistically different. Furthermore, the difference in all statistical properties of sEMG signals is significant (p < 0.05). Separability indices reveal that motion classes are more diverse in the home setting. In summary, differences in sEMG signals generated between home and laboratory only affect between-calibration performance.

Selective Grafting of Protease-Resistant Adhesive Peptides on Titanium Surfaces.

In orthopedic, dental, and maxillofacial fields, joint prostheses, plates, and screws are widely used in the treatment of problems related to bone tissue. However, the use of these prosthetic systems is not free from complications: the fibrotic encapsulation of endosseous implants often prevents optimal integration of the prostheses with the surrounding bone. To overcome these issues, biomimetic titanium implants have been developed where synthetic peptides have been selectively grafted on titanium surfaces via Schiff base formation. We used the retro-inverted sequence (DHVPX) from [351-359] human Vitronectin and its dimer (D2HVP). Both protease-resistant peptides showed increased human osteoblast adhesion and proliferation, an augmented number of focal adhesions, and cellular spreading with respect to the control. D2HVP-grafted samples significantly enhance Secreted Phosphoprotein 1, Integrin Binding Sialoprotein, and Vitronectin gene expression vs. control. An estimation of peptide surface density was determined by Two-photon microscopy analysis on a silanized glass model surface labeled with a fluorescent analog.

Evaluation of the CNC® prosthetic system in recurrent breast cancer patients with chemotherapy-induced alopecia: a pilot study

BackgroundChemotherapy-induced alopecia (CIA), although generally reversible, is felt as extremely distressing by patients with breast cancer. A certified medical device (Capelli Naturali a Contatto®—CNC®) was produced to provide patients with a personalized scalp prosthesis, reproducing the patient’s original hair, resistant to any type of everyday or sporting activity, and hairdressing.AimsThe present study aimed to evaluate the impact of the CNC® device on the patient’s perception of their body image, psychological wellbeing, satisfaction, strengths and weakness of the CNC® device.MethodA pilot study was carried out on 21 patients affected by CIA due to recurrent breast cancer. A mixed quantitative/qualitative method was used, including administering a questionnaire and a focus group.ResultsBased on the Body Image Scale, body image perception improved after 3 and 6 months using the device in the 20 patients who answered the questionnaire. No significant change over time emerged for the six dimensions investigated by the Italian version of the Psychological Well-Being Scale. The thematic analysis of the focus groups showed six themes: definition of the prosthetic device, acceptance of the proposal, experience with the conventional wig, strengths, weaknesses, economic issues.ConclusionCompared to the previous experience of CIA and the standard wig, the use of the CNC® device improved everyday life and may be proposed to women undergoing chemotherapy and expecting alopecia to prevent discomfort, social embarrassment, and compromised body image.

Evaluation of the effects of focused ultrasound stimulation on the central nervous system through a multiscale simulation approach.

The lack of sensory feedback represents one of the main drawbacks of commercial upper limb prosthesis. Transcranial Focused Ultrasound Stimulation (tFUS) seems to be a valid non-invasive technique for restoring sensory feedback allowing to deliver acoustic energy to cortical sensory areas with high spatial resolution and depth penetration. This paper aims at studying in simulation the use of tFUS on cortical sensory areas to evaluate its effects in terms of latency ad firing rate of the cells response, for understanding if these parameters influence the safety and the efficacy of the stimulation. In this paper, in order to study the propagation of the ultrasound wave from the transducer to the cortical cells, a multiscale approach was implemented by building a macroscopic model, which estimates the pressure profile in a simplified 2D human head geometry, and coupling it with the SONIC microscale model, that describes the electrical behaviour of a cortical neuron. The influence of the stimulation parameters and of the skull thickness on the latency and the firing rate are evaluated and the obtained behaviour is linked to the sensory response obtained on human subjects. Results have shown that slight changes in the transducer position should not affect the efficacy of the stimulation; however, high skull thickness leads to lower cells activation. These results will be useful for evaluating safety and effectiveness of tFUS for sensory feedback in closed-loop prosthetic systems.

The relationship between residual limb health, motion within the socket, and prosthetic suspension.

Residual limb health is critical for continued prosthesis use; however, many prosthesis users experience skin-related breakdown. The interface between the residual limb and the prosthetic socket sets the local mechanical environment and plays a role in skin stresses. Motion of the residual limb in the socket adds additional mechanical strain on the limb. This article explores the relationship between motion of the limb in the socket and residual limb health. We evaluated current methods for assessing residual limb health and motion of the residual limb in the socket and compared these evaluations across different prosthetic suspension systems. While few direct studies comparing residual limb health and motion exist, it has been shown that elevated vacuum suspension systems result in both improved residual limb health compared to passive suction and pin-lock systems and decreased motion compared to passive suction, pin-lock, knee sleeve, and anatomical suspension systems. While motion and health have not been directly linked, elevated vacuum suspension may demonstrate a relationship that reduced motion of the residual limb in the socket improves residual limb health. Further evaluation in this area is necessary to more completely and directly understand the relationship between residual limb motion and residual limb health.

Twenty-four months of bacterial colonialization and infection rates in patients with transcutaneous osseointegrated prosthetic systems after lower limb amputation-A prospective analysis.

Transcutaneous osseointegrated prosthesis systems (TOPS) are alternative rehabilitation methods to socket prosthetics, after limb amputation. TOPS compromise a two-step surgery: starting with the implantation of the stem which is then followed by the creation of the transcutaneous stoma through which the exoprosthesis can be connected. Immediately after surgery, this opening is permanently exposed to pathogens. This study aimed to investigate the dynamics of bacterial colonization of the stoma to analyze whether obligate bacterial colonization leads to a risk of periprosthetic infections after TOPS treatment. This prospective study analyzed data from 66 patients (aged 26-75 years) after TOPS treatment between 2017 and 2019. Microbiological swabs from the stoma were analyzed on the first postoperative day and 3, 6, 12, and 24 months after stoma creation. Infection rates, laboratory values (CRP, leukocyte count, hemoglobin), and body temperature were recorded at these points in time. Statistical analysis was performed using SPSS 28. The results show the formation of a stable environment dominated by Gram-positive bacteria in the stoma of TOPS patients over 24 months. Staphylococcus aureus, Staphylococcus spp., and Streptococcus spp. were the most common species found. With regard to the cohort up to the 3 months follow-up, 7.9% (five patients) developed infections surrounding the TOPS procedure. In relation to the whole cohort with loss to follow-up of 80.3% at the 24 months follow-up the infection rates increased up to 38.3%. The soft tissue inside and around the transcutaneous stoma is colonialized by multiple taxa and changes over time. A stable Gram-positive dominated bacterial taxa could be a protective factor for ascending periprosthetic infections and could possibly explain the relatively low infection rate in this study as well as in literature.

A multiarticulate pediatric prosthetic hand for clinical and research applications.

Although beginning to emerge, multiarticulate upper limb prostheses for children remain sparse despite the continued advancement of mechatronic technologies that have benefited adults with upper limb amputations. Upper limb prosthesis research is primarily focused on adults, even though rates of pediatric prosthetic abandonment far surpass those seen in adults. The implicit goal of a prosthesis is to provide effective functionality while promoting healthy social interaction. Yet most current pediatric devices offer a single degree of freedom open/close grasping function, a stark departure from the multiple grasp configurations provided in advanced adult devices. Although comparable child-sized devices are on the clinical horizon, understanding how to effectively translate these technologies to the pediatric population is vital. This includes exploring grasping movements that may provide the most functional benefits and techniques to control the newly available dexterity. Currently, no dexterous pediatric research platforms exist that offer open access to hardware and programming to facilitate the investigation and provision of multi-grasp function. Our objective was to deliver a child-sized multi-grasp prosthesis that may serve as a robust research platform. In anticipation of an open-source release, we performed a comprehensive set of benchtop and functional tests with common household objects to quantify the performance of our device. This work discusses and evaluates our pediatric-sized multiarticulate prosthetic hand that provides 6 degrees of actuation, weighs 177g and was designed specifically for ease of implementation in a research or clinical-research setting. Through the benchtop and validated functional tests, the pediatric hand produced grasping forces ranging from 0.424-7.216N and was found to be comparable to the functional capabilities of similar adult devices. As mechatronic technologies advance and multiarticulate prostheses continue to evolve, translating many of these emerging technologies may help provide children with more useful and functional prosthesis options. Effective translation will inevitably require a solid scientific foundation to inform how best to prescribe advanced prosthetic devices and control systems for children. This work begins addressing these current gaps by providing a much-needed research platform with supporting data to facilitate its use in laboratory and clinical research settings.

Progress in Developing an Emulation of a Neuromorphic Device That Is Predicted to Enhance Existing Cortical Prosthetic Vision Technology by Engaging Desired Visual Geometries

The utility of currently available cortical prosthetic vision systems is disappointing. The essential features of a neuromorphic device that is predicted to enhance vision provided by available systems follow from a hypothesis which states that the objective and subjective aspects of cortical prosthetic vision jointly constitute patterns that emerge from specified synaptic interactions. The research reported here completes several required steps in developing an emulation of this device: (1) replication of small-scale simulations that are consistent with the hypothesis using the NEST (Écublens, Vaud, Switzerland) simulator, which can also be used for full-scale network emulation by a neuromorphic computer; (2) testing whether results consistent with the hypothesis survive increasing the scale and duration of simulations; (3) establishing a method that uses numbers of spikes produced by network neurons to report the number of phosphenes produced by cortical stimulation; and (4) simulating essential functions of a neuromorphic device which is predicted to enhance current prosthetic systems. NEST simulations replicated early results and increasing their scale and duration produced results consistent with the hypothesis. A decision function created using multinomial logistic regression correctly reported the expected number of phosphenes for three sets of 2080 spike number distributions in which half of each set arises from simulations expected to yield continuous visual forms by engaging a desired visual geometry. A process for modulating electrical stimulation amplitude based on intermittent population recordings that is predicted to produce desired visual geometries was successfully simulated. Implications of these results for future research are discussed.

Classification of Different Shoulder Girdle Motions for Prosthesis Control Using a Time-Domain Feature Extraction Technique

Abstract—The upper limb amputation exerts a significant burden on the amputee, limiting their ability to perform everyday activities, and degrading their quality of life. Amputee patients’ quality of life can be improved if they have natural control over their prosthetic hands. Among the biological signals, most commonly used to predict upper limb motor intentions, surface electromyography (sEMG), and axial acceleration sensor signals are essential components of shoulder-level upper limb prosthetic hand control systems. In this work, a pattern recognition system is proposed to create a plan for categorizing high-level upper limb prostheses in seven various types of shoulder girdle motions. Thus, combining seven feature groups, which are root mean square, four-order autoregressive, wavelength, slope sign change, zero crossing (ZC), mean absolute value, and cardinality. In this article, the time-domain features were first extracted from the EMG and acceleration signals. Then, the spectral regression (SR) and principal component analysis dimensionality reduction methods are employed to identify the most salient features, which are then passed to the linear discriminant analysis (LDA) classifier. EMG and axial acceleration signal datasets from six intact-limbed and four amputee participants exhibited an average classification error of 15.68 % based on SR dimensionality reduction using the LDA classifier.