Design overview
The proposed socket solution consists of a soft silicone structure that integrates a rigid frame of epoxy resin reinforced with carbon fibers (Figure 1). The rigid structure features a distal hemispheric shape with two parts extended to the proximal area only in the lateral and medial sites. This solution removes a rigid interface in the anterior and posterior regions, thus improving comfort especially in sitting positions. At the same time, it allows for a narrower medio-lateral structure, fundamental for the alignment of the femur with the prosthesis axis, but also for a more flexible prosthetic interface able to compensate for small changes in the volume of the residual limb.7
Based on the user’s preferences and features, the proximal edges of the rigid frame can be featured by a sub-ischial design, i.e. with the proximal edge few centimeters under the ischium,31 or they can create a grip on the great trochanter and on the ischial ramus or tuberosity, as in more traditional Ischial Containment Sockets.32 The rigid frame is embedded in a soft structure made of 617H43 Silicone Gel (Ottobock). This is constituted by a 5 mm outer layer and an inner layer with a variable thickness increasing from 5 mm proximally to 15 mm distally, thus simulating the shape of commercial prosthetic stand-alone liners, but integrated into the socket itself.
For the assessment of this new design, an expert prosthetist manufactured a preliminary soft socket without smart elements to quantify its performance in terms of volume compensations. Since the socket is a user-specific component that replicates the residual limb shape, it was designed for a transfemoral residual limb simulator with an embedded fluidic chamber that allows for changing its volume.33 Subsequently, a transfemoral amputee was recruited to manufacture a new personalized soft socket integrating the sEMG sensing and augmenting feedback.
Volume compensation performances of the soft socket in simulated environment
A 3D scanner (model: GO!SCAN50, Creaform Inc.) was used to measure the initial simulator volume ( ), which was found equal to 5000 cm3. Then, the simulator was fixed in position within the new soft socket (Figure 2, left) and 150 cm3 of water ( ) were supplied to the fluidic chamber at a flow rate of 50 cm3/min by a syringe pump. This allowed for a simulator volume increment of 3% , which has been found as a characteristic volume fluctuation in the transfemoral amputee population.26 An F – Socket system (Tekscan Inc.) with two resistive sensors (model: Tekscan Medical Sensor 9833) was positioned on the lateral and medial sites of the simulator / socket interface to measure the pressure changes during the volume increment. This test was repeated five times with the new soft socket (Figure 2, left) and five times with a more traditional one (Figure 2, right). In particular, for the traditional one, the Northwestern University Flexible Sub-ischial Vacuum (NU-FlexSIV) Socket was chosen, since reported as the most comfortable and flexible solution in the current state-of-the-art (Figure 2, right).31
sEMG sensing system
The sEMG sensing system consisted of four pairs of commercial surface pre-gelled electrodes (model: Ag/AgCl PSG50S) connected to a commercial 64-channel detection device (model: Sessantaquattro, OT Bioelettronica S.r.l.) (Figure 1). The sEMG signals allowed for the detection of the user’s motor intention by the implementation of the algorithm presented in Barberi et al.13 The algorithm received as input the Mean Absolute Value (MAV) extracted from the preprocessed sEMG signals of a subfraction of the gait cycle and it outputted the motor task the subject desired to perform. The detectable motor tasks were Ground Level Walking (GLW), Stairs Ascending (SA), Stairs Descending (SD), Ramps Ascending (RA), and Ramps Descending (RD). The classification process exploited a Support Vector Machine (SVM) based on the Error Correcting Output Code (ECOC) technique.
For decoding the user’s motor intentions, the Rectus Femoris, Tensor Fasciae Latae, Adductor Longus, and Biceps Femoris were proved as optimal recording sites in a previous clinical study.10,13 The positions of the four electrode pairs on these target residual muscles were identified in the recruited subject’s residual limb with the help of a physical therapist and recorded by 3D scanning (Figure 3, top). An expert prosthetist made the negative cast of the residual limb with plaster bandages and realized the corresponding positive cast, which was 3D scanned, thus identifying the final positions of the electrode connectors at the prosthetic interface (Figure 3, bottom).
Augmenting feedback system
The augmenting feedback system included three VibroTactile (VT) units, each consisting of an eccentric rotating mass (ERM) motor (Pico Vibe 304-116, Precision MicroDrives) encapsulated in a PDMS silicone disk (diameter: 20 mm; thickness 6 mm) for enhancing users’ comfort (Figure 1).34 The VT units were controlled by a central unit, namely the VibroBoard, through the FPGA (Field Programmable Gate Array) of a sbRIO-9651 System on Module (National Instruments Corp.). Each VT unit was driven by a 1 kHz PWM (Pulse-Width Modulation) of a 5V source. The VibroBoard unit was housed in a case that can be worn by a belt to not hinder the user’s mobility. The VTs were instead embedded into the soft socket, where housings in the proximal area were manufactured on purpose.
Given the strong subject-specific perceptual properties of the residual limb, the positioning of the VTs was previously assessed. In particular, the posterior region was not considered for the delivery of the stimuli, to prevent affecting the user’s comfort when sitting. Hence, the lateral, anterior, and medial sides along the circumference of the proximal residual limb were chosen for VT1, VT2, and VT3, respectively. Considering the final configuration of the EMG electrodes and the need to put the VTs far enough from them (at least 2 cm) to prevent affecting the quality of the signals, the final spacing was about 11 cm and 7.5 cm between VT1 and VT2 and between VT2 and VT3, respectively.
Thermal conditions of the residual limb within the socket
Nine commercial Hygrochron Data Logger iButtons (Maxim Integrated Products, Inc; model: DS1923-F5#; 11-bit resolution: 0.0625 °C for temperature and 0.04% for relative humidity; sampling time: 5 s)35 were also temporarily integrated into the socket after having been calibrated by a climatic chamber.16 The iButtons allowed for measuring both temperature and relative humidity of the residual limb skin within the socket. In particular, they were positioned into specific housings manufactured at the socket interface proximally and distally in the anterior, posterior, lateral, and medial sites, and at the distal end (see Figure 5). After the thermal characterization, the iButtons were removed and the housings were filled with silicone gel buttons to recreate a smooth interface.
Human participant tests
The general features of the recruited transfemoral amputee are reported in TABLE 1. The design study was approved by the Joint Ethical Committee of the Scuola Superiore Sant’Anna and Scuola Normale Superiore (Approval no. 11/2021) and the tests were carried out at the prosthetic center Franchi Ortopedia (Cascina, PI, Italy). All experiments were undertaken in accordance with the World Medical Association’s Code of Ethics and the Declaration of Helsinki. The recruited subject signed an informed consent form to take part in the test sessions.
TABLE 1 Subject’s general features
Sex
|
Man
|
Age
|
50 years old
|
Time since amputation
|
32 years
|
Amputation cause
|
Traumatic event
|
Own socket design
|
Ischial Cointainment Socket of carbon fiber reinforced epoxy resin without liner
|
Suspension system
|
Passive vacuum suspension based on unidirectional valve
|
Activity level
|
K3*
|
*K level: rating system used to indicate the individual’s potential functional ability. K1: no ability to ambulate; K2: able to perform activities typical of limited community ambulatory; K3: able to perform activities typical of community ambulatory; K4: able to perform high-impact activities.
During tests, the subject was asked to perform a circuit training including the five different locomotion tasks that the motion intention decoding algorithm could infer (i.e., GLW, SA, SD, RA, and RD, Figure 4). This circuit training was repeated 15 times with a 5-minute break in between to avoid the subject’s fatigue. During trials, the gait events were identified using signals of a footswitch placed under the subject’s foot. At the beginning and end of the experimental session and between the two trials, the sEMG signals were recorded during four muscle contractions in a standing position to perform a further Signal-to-Noise Ratio (SNR) analysis.
For the post-processing of the acquired data, three representative subwindows of the gait cycle were selected to test the efficacy of the algorithm presented in Barberi et al.13 Each subwindow provided the classification output at a different percentage of the ongoing step. The first one considered the EMG signals acquired between 0% and 100% of the gait cycle, the second between 0% and 60%, and the third between -30% and 20%. In the latter case, the intention decoding algorithm used the last portion of the previous gait cycle. Each window was tested by performing a 5-fold cross-validation on the acquired dataset, after the implementation of the preprocessing and feature extraction pipeline described in Section IIA-b. The tests were repeated with two different SVM kernels (linear and 2nd-order polynomial) to show the algorithm behavior when using approaches that present different computational loads.
The appropriate integration of the augmenting feedback device into the socket was assessed through a psychophysical experiment. Specifically, the recruited subject was asked to localize random stimuli delivered over the residual limb surface while wearing the socket in either the sitting or standing position. After a familiarization session, where the experimenter provided the subject with known vibration events, five experimental trials were performed. The activations of one, two, three, or no VT units at three different intensities (i.e., 50%, 70%, and 100% PWM, corresponding to a ERM motor acceleration peak of about 2g, 3g and 4g, respectively) were repeated four times per trial, having 96 stimuli each and 480 stimuli overall during the experiment. To avoid expectation effects, the delivery of the stimuli was randomized across the trials. At each event, the subject was asked to identify the vibrating units and the experimenter recorded the answers on a dedicated LabVIEW (National Instruments Corp) GUI (Graphical User Interface). A 5-minute rest period between consecutive trials was taken to avoid the subject’s fatigue. The collected data were analyzed in MATLAB (Mathworks, Inc) to assess the subject’s accuracy to localize the stimuli, computed as the number of the right answers over the total amount of the delivered feedback events per each intensity level. The normalized confusion matrices for both the postures were also extrapolated to identify the misclassification among all the activation conditions for all the stimulation levels grouped together.
Finally, the intra-socket temperature and relative humidity were collected by the nine iButtons during 30 minutes of resting, 20 minutes of walking at a self-selected speed on a treadmill, and 30 minutes of resting post-exercise. The mean and standard deviation of the nine sensors were evaluated on the initial and final 1-minute recorded data of the two resting periods. Then, the subject was asked to fill out the Socket Comfort Score (SCS)36 and a modified Prosthetic Evaluation Questionnaire (PEQ) focused on the comfort and fitting of the prosthetic socket.37,38
Manufacturing
The manufacturing of the soft socket started with the development of the rigid frame, when the positive cast of the residual limb was done (Figure 3). In particular, a CAD of the inner variable thickness silicone layer of the socket was designed in SolidWorks starting from the 3D scan of the positive cast (Figure 6a). It was 3D printed in PLA material together with a valve template required for the integration of the suspension system based on a unidirectional valve (21Y14, Ottobock) at the lateral-distal area of the socket.7 These components were positioned on the residual limb positive cast and the rigid frame of carbon fiber reinforced epoxy resin was manufactured by lamination technique.
Similarly, a specific mold for the socket soft structure and the templates of the VT units and iButtons were developed (Figure 6b). The 3D printed variable thickness part was removed and the templates, the rigid frame, and the electrode connectors were fixed on the positive cast and subsequently closed within the 3D printed mold. Afterward, the soft structure was manufactured by injecting the silicone gel within the mold.
When the silicone gel was polymerized, the external silicone layer on the rigid frame was cut to minimize the weight of the final system and an esthetic sock was glued to the external surface. The final soft socket is shown in Figure 7.