This dataset contains electromyography (EMG) signals for use in human-computer interaction studies. The dataset includes 4-channel surface EMG data from 40 participants with an equal gender distribution. The gestures in the data are rest or neutral state, extension of the wrist, flexion of the wrist, ulnar deviation of the wrist, radial deviation of the wrist, grip, abduction of all fingers, adduction of all fingers, supination, and pronation. Data were collected from 4 forearm muscles when simulating 10 unique hand gestures and recorded with the BIOPAC MP36 device using Ag/AgCl surface bipolar electrodes. Each participant's data contains five repetitive cycles of ten hand gestures. A demographic survey was applied to the participants before the signal recording process. This data can be utilized for recognition, classification, and prediction studies in order to develop EMG-based hand movement controller systems. The dataset can also be useful as a reference to create an artificial intelligence model (especially a deep learning model) to detect gesture-related EMG signals. Additionally, it is encouraged to use the proposed dataset for benchmarking of current datasets or for validation of machine learning and deep learning models created with different datasets in accordance with the participant-independent validation strategy.

The Physical Action Data Set includes 10 normal and 10 aggressive physical actions that measure the human activity. This dataset consists of EMG signals recorded from 8 total muscles; the biceps and triceps of both the arms and the hamstrings and thigh muscles of both legs.The data have been collected by 4 subjects using the Delsys EMG wireless apparatus.

Additional Information

Protocol: Three male and one female subjects (age 25 to 30), who have experienced aggression in scenarios such as physical fighting, took part in the experiment. Throughout 20 individual experiments, each subject had to perform ten normal and ten aggressive activities. Regarding the rights of the subjects involved, ethical regulations and safety precaution have been followed based on the code of ethics of the British psychological society. The regulations explain the ethical legislations to be applied when experiments with human subjects are conducted. According to the experimental setup and the precautions taken, the ultimate risk of injuries was minimal. The subjects were aware that since their involvement in this series of experiments was voluntary, it was made clear that they could withdraw at any time from the study.

Instrumentation: The Essex robotic arena was the main experimental hall where the data collection took place. With area 4x5.5m, the subjects expressed aggressive physical activities at random locations. A professional kick-boxing standing bag has been used, 1.75m tall, with a human figure drawn on its body. The subjects’ performance has been recorded by the Delsys EMG apparatus, interfacing human activity with myoelectrical contractions. Based on this context, the data acquisition process involved eight skin-surface electrodes placed on the upper arms (biceps and triceps), and upper legs (thighs and hamstrings).

Data Setup: The overall number of electrodes is 8, which corresponds to 8 input time series one for a muscle channel (ch1-8). Each time series contains ~10000 samples (~15 actions per experimental session for each subject). More: readme.txt

This dataset contains 2 sets of sEMG recordings: a set containing PINCH movements (4 pinches between thumb and index/middle/ring/pinky finger) and a set containing ROSHAMBO movements (3 movements: rock, paper, scissors). Both sets have been recorded with the Myo armband. The Myo is composed of 8 equally spaced non-invasive sEMG sensors that can be placed approximately around the middle of the forearm. The sampling frequency of Myo is 200 Hz. The output of the Myo is a.u.. The PINCH set contains recordings of 22 subjects whilst the ROSHAMBO set contains recordings of 10 subjects. Each subject performed 3 sessions, where each hand gesture was recorded 5 times, each lasting for 2s. Between the gestures a relaxing phase of 1s is present where the muscles could go to the rest position, removing any residual muscular activation. Full details for the ROSHAMBO set can be found in:

Donati, Elisa, et al. "Processing EMG signals using reservoir computing on an event-based neuromorphic system." 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2018.

For each session, the dataset contains 2 *.npy files one specifying the EMG data (*_emg.npy) the other one (*_ann.npy) specifying the corresponding gestures along the sampled EMG. The data can be easily loaded in python with numpy.

impacting generalization. Here we developed EMGNet

EMG dataset for gesture recognition with arm translation

https://doi.org/10.5061/dryad.8sf7m0czv

Corresponding author: Iris Kyranou, email: iriskyr@gmail.com

Description of the data and file structure

Subjects

8 intact subjects.

Acquisition Setup

The sEMG data are acquired using four Trigno Quattro Sensors (https://delsys.com/trigno-quattro/), while kinematic data are acquired using a Cyberglove II data glove (http://www.cyberglovesystems.com/cyberglove-ii).

The EMG data collection process involves the following steps:

• Surface EMG (sEMG) signals sampled at 2kHz
• Skin cleaned with 70% isopropyl alcohol before electrode placement
• 16 electrodes arranged in two rows of 8 around the forearm:
  • First row: Starting at extensor carpi ulnaris muscle
  • Second row: Positioned between the electrodes of the f...

EMG dataset from 7 individuals learning different percentages of maximum voluntary contraction using different modalities of sensory feedback.

This dataset contains surface electromyography (sEMG) data of 5 different hand gestures performed by eight subjects. The data is store in a folder for each one of the subjects, each folder, contains 5 files, one for each gesture. The files contain the sEMG data of 4 sEMG channels placed on the forearm. The gestures are: open hand, closed hand, lateral pinch, signaling sign, rock sign. A file with a demo Recurrent Neural Network is also included.

The reader may refer to the following articles for more information about the dataset: LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals and A Proposal of Bioinspired Soft Active Hand Prosthesis.

AnasElkhabbaz/EMG-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

EMG signal collected using Arduino. We designed a simulation testbed for three classes of EMG signals namely resting state, full five-finger (full palm) movement, and last class is individual finger movement (i.e. thumb movement). This simulation testbed was designed based on actual EMG sensor readings obtained through Arduino connected with sensors (EMG v2).

This is the data used in paper "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG" DOI: 10.1109/NILES50944.2020.9257909

Data can be found as a txt files for each subject separately or can be found as .mat file with all subjects included.

Data details:

• Sampling Frequency= 200 Hz
• 8-Bit resolution
• 15 Healthy Subjects
• 6 Kg Load with elbow flexed to a 90 angle
• 120 Seconds Duration
• 8 channels sEMG
• 50 Hz Notch Filtered

For more details and citation:

A. Ebied, A. M. Awadallah, M. A. Abbass and Y. El-Sharkawy, "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG," 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 423-427, doi: 10.1109/NILES50944.2020.9257909.

A database was created in .XLSX and .CSV formats containing the processing of an EMG signal and the position and angle error during the execution of three dynamic tasks based on the three-dimensional movement of the upper limb. This data was recorded from the quantification of the hand position error.

This repository contains raw surface Electromyography signals termed surface Electromyograms (sEMG) recorded with 8 circular surface Ag/AgCl pairs of electrodes placed circumferentially around the forearm of the dominant arm in 10 able-bodied individuals (5 Females and 5 Males). The proposed method for processing sEMG data with subjects' characteristics and protocol can be found in Miljković & Isaković 2021.

For each subject, sEMG was recorded from three recording electrode array positions termed P1, P2, and P3 for 9 hand movements. We provide a compressed .7z folder with 10 sub-folders for each subject named by subject ID (ID1, ID2, ... ID10). Each sub-folder contains 27 .txt data files (for 9 movements × 3 electrode array positions), except for subject ID7 (there are 24 .txt records, since three records for wrist extension EX in P1, P2, and P3 positions got corrupted in subject ID7). Average size of 10 sub-folders is 167.50 ± 27.02 MB with maximum of 194 MB and minimum of 117 MB.

The subjects performed following hand movements from the reference resting position –relaxation, R (explained in-detail in Miljković & Isaković 2021): (1) spherical power grasp, PS, (2) three finger sphere grasp, 3F, (3) two finger prismatic grasp, PP, (4) wrist flexion, FL, (5) wrist extension, EX, (6) radial deviation, RD, (7) ulnar deviation, UD, and then forearm rotation i.e. (8) pronation, PR, and (9) supination, SU. PS, 3F, PP, FL, EX, RD, UD, PR, and SU correspond to type of hand movement in naming convention for .txt data files.

Hand movements YoutTube playlist contains explanatory videos for 9 hand movements recorded in this study, and we also provide corresponding .wmv here in the "movies hand movements.7z". Naming convention for .wmv files is type of hand movement with both full name and abbreviation for the movement (for example "radialDeviation-RD.wmv").

Naming convention for .txt data files within 10 sub-folders is: subjects ID _ type of hand movement _ recording electrode array position (for example: "ID1_3F_P1.txt" in sub-folder ID1, "ID9_RD_P3.txt" in sub-folder ID9).

Dataset contents

EMG dataset.7z, 267 .txt data files, text format

movies hand movements.7z, 9 .wmv files, explanatory hand movement videos (also available on YouTube)

README.txt, metadata for data files, text format

Data files contain numerical values with decimal point* according to the following structure

column - CH1** (recorded samples from channel 1)

column - CH2** (recorded samples from channel 2)

column - CH3** (recorded samples from channel 3)

column - CH4** (recorded samples from channel 4)

column - CH5** (recorded samples from channel 5)

column - CH6** (recorded samples from channel 6)

column - CH7** (recorded samples from channel 7)

column - CH8** (recorded samples from channel 8)

• For subjects ID1 and ID2 three decimal places are provided, while for other subjects 6 decimal places in .txt data files are provided.

** Each data file contains at least 10 repetitions of the corresponding movement. In cases where file contains >10 repetitions (overall 162 .txt data files), we used the first or the last ten for the analysis (except for two files where short and strong artifact appeared during the measurement procedure, and corresponding movement repetitions were discarded) presented in Miljković & Isaković 2021.

Sample rate was set at 1000 Hz and A/D card had 16 bits resolution. Gain of the amplifier was set at 1000. For more in-detail explanations of electrode array assemble and positioning for sEMG channels CH1, CH2, ... CH8, please refer to Miljković & Isaković 2021.

If you find these signals useful for your own research or teaching class, please cite both relevant preprint and dataset as:

Miljković, N. and Isaković, M.S., 2021. Effect of the sEMG electrode (re) placement and feature set size on the hand movement recognition. Biomedical signal processing and control, 64:102292. 10.1016/j.bspc.2020.102292

Miljković, N. and Isaković, M.S., 2020. Surface electromyogram (sEMG) dataset recorded from forearm for 9 hand movements and three electrode array positions. [Data set]. Zenodo 10.5281/zenodo.4039550.

ACKNOWLEDGEMENTS (from Miljković & Isaković 2021): "Special appreciation the authors owe to Professor Mirjana B. Popović from the University of Belgrade for her kind support,precious guidance, and advice regarding this research which significantly improved the manuscript. Also, the authors would like to thank Dr Matija Štrbac from Tecnalia Serbia Ltd. for providing advice throughout the study.The authors thank all volunteers for their participation."

The dataset includes three files, FM.mat, rawEMGData_H.mat, and rawEMGData_P.mat. FM.mat is the upper limb FM score of 20 stroke patients.rawEMGData_H.mat and rawEMGData_P.mat are the raw EMG signals of healthy subjects and stroke patients when performing hand-to-nose movement. Ten muscle activities are measured during the movements, triceps brachii long and lateral head (TLO and TLA), latissimus dorsi (LAT), pectoralis major (PEC), deltoid anterior, medial, and posterior (DEA, DEM, and DEP), trapezius upper (TRA), biceps brachii (BIC), and brachioradialis (BRA). And each subject continuously repeated the movement three times.

Our CapgMyo database includes HD-sEMG data for 128 channels acquired from 23 intact subjects by using our newly developed acquisition device. The acquisition device has a matrix-type (8×16) differential electrode array with silver wet electrodes. The CapgMyo database consists of 3 sub-databases (DB-a, DB-b and DB-c); 8 isometric and isotonic hand gestures were obtained from 18 of the 23 subjects in DB-a. When using this data, please cite the publication: Geng W, Du Y, Jin W, et al. Gesture recognition by instantaneous surface EMG images[J]. Scientific reports, 2016, 6: 36571.

Motivation

EMG-driven exoskeleton assistance requires the use of intention detection models to associate electromyographic signals with some feature of human movement, such as angular position, velocity, or joint torque. The goal of this dataset is to provide data that allows the benchmarking of such models for a variety of movements.

Short description

This dataset includes kinematic, dynamic and electromyographic data from 17 participants (11 males, age 28.2 ± 7 years, height 175.4 ± 7 cm, weight 70 ± 11 kg). These data were collected during the performance of a sagittal plane upper limb tracking task for single joint (elbow flexion/extension) and multiple joint (elbow and shoulder flexion/extension).

Methodology

A detailed description of the data collection methodology can be found here: https://www.biorxiv.org/content/10.1101/2024.01.11.575155v1

Data Description

The data set consists of 17 folders, one for each participant. Inside each folder you will find

• A metadata file (SXX.json) containing information about the subject: age, sex, weight, height, and upper limb masses and lengths.

• An OpenSim model file (scaledModel.osim) containing a scaled upper limb model of the given subject.

• A MVC folder containing EMG data from maximal voluntary contraction trials

• A SJ folder containing trial folders for the single joint condition (elbow flexion/extension).

• A MJ folder containing test folders for the multi-joint condition (elbow and shoulder flexion/extension)

EMG Data

The files containing EMG data have the following header

TIME,DELTAnt,DELTMed,DELTPost,PECT,LATI,TRILong,TRILat,TRIMed,BICLong,BICShort,BRA,BRD

This corresponds to a time stamp and EMG signals from the anterior, median and posterior detloids, pectoralis major, latissimus dorsi, long, lateral and median triceps, long and short biceps, brachioradialis and brachialis.

MVC data

The MVC folder contains two files: emgMVCElbow.csv and emgMVCShoulder.csv. They were collected during the realisation of maximum voluntary contraction tasks and contain raw EMG data sampled at 2 kHz.

Trial data

Single-joint condition

Single-joint trials contain 5 files:

• emgFilt.csv: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.

• emgRaw.csv: Raw EMG signals. Sampled at 2 kHz.

• humanPositions.csv: Angular position of the elbow in rad. Sampled at 100 Hz.

• humanVelocities.csv: Angular velocity of the elbow in rad/s. Sampled at 100 Hz.

• muscleTorque.csv: Joint torque of the elbow in N.m. Sampled at 100 Hz.

Multi-joint condition

Multi-joint trials contain 5 files:

• emgFilt.csv: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.

• emgRaw.csv: Raw EMG signals. Sampled at 2 kHz.

• humanPositions.csv: Angular positions of the upper limb in rad. Sampled at 100 Hz.

• humanVelocities.csv: Angular velocities of the upper limb rad/s. Sampled at 100 Hz.

• muscleTorque.csv: Joint torques of the upper limb in N.m. Sampled at 100 Hz.

For this trial, kinematic and torque files use the following headers:

TIME,elv_angle,shoulder_elv,shoulder_rot,elbow_flexion,pro_sup,deviation,flexion

The columns correspond to the OpenSim model coordinates. For sagittal plane movement, columns of interest are shoulder_elv for shoulder flexion/extension and elbow_flexion for elbow flexion/extension.

Snapshot img

Electromyography (EMG) Devices Market Size 2024-2028

The electromyography (EMG) devices market size is forecast to increase by USD 403.2 million, at a CAGR of 7.1% between 2023 and 2028.

The market is witnessing significant growth due to the increasing prevalence of neuromuscular disorders and the rising use of prosthetic devices. Neuromuscular disorders, such as myasthenia gravis, muscular dystrophies, and peripheral neuropathies, are on the rise, leading to a surge in demand for diagnostic and therapeutic devices, including EMG devices. Moreover, the integration of EMG technology in prosthetic devices is revolutionizing the rehabilitation process for amputees, further fueling market growth. However, the market faces challenges, primarily due to the limitations of EMG devices. These limitations include the invasive nature of some EMG procedures, which may cause discomfort and pain to patients.
Additionally, the high cost of EMG devices and the complex nature of their operation may hinder market penetration, particularly in developing regions. Companies operating in the EMG Devices Market must address these challenges by developing non-invasive alternatives and offering affordable pricing models to expand their reach and capture a larger market share.

What will be the Size of the Electromyography (EMG) Devices Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free Sample

The market is characterized by continuous evolution and dynamic market activities. EMG signal decomposition plays a crucial role in neurological assessment, providing valuable insights into muscle activation patterns. Electromyographic sensors, available in various forms such as needle and surface electrodes, are integral to EMG data acquisition. Wireless EMG systems and integrated EMG systems are gaining popularity due to their portability and convenience. Clinical EMG applications span from nerve conduction studies to muscle fiber conduction, muscle fatigue detection, and impedance measurements. These applications find extensive use in rehabilitation centers, orthopedic clinics, and research institutions. EMG signal filtering and interpretation are essential steps in EMG data processing.

Artifact reduction techniques are continually being developed to enhance the accuracy of EMG data. EMG biofeedback devices offer therapeutic benefits by enabling real-time muscle activity monitoring and control. The ongoing advancements in EMG technology are expanding its applications in various sectors. Prosthetics, for instance, are being enhanced with EMG sensors to improve user experience and functionality. The development of portable EMG devices has made neurological assessments more accessible and convenient. In summary, the EMG devices market is marked by continuous innovation and growth. The integration of EMG technology in various sectors, from healthcare to prosthetics, underscores its importance and potential for future developments.

How is this Electromyography (EMG) Devices Industry segmented?

The electromyography (EMG) devices industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Hospitals and clinics
  Rehabilitation centers
  Homecare


Modality

  Stationary
  Portable


Geography

  North America

    US


  Europe

    France
    Germany


  APAC

    China
    Japan


  Rest of World (ROW)

By End-user Insights

The hospitals and clinics segment is estimated to witness significant growth during the forecast period.

The EMG devices market encompasses various applications, including neurological assessment, rehabilitation, prosthetics, and orthopedics. EMG signal decomposition plays a crucial role in understanding muscle activation patterns, leading to advancements in muscle fiber conduction studies, muscle fatigue detection, and impedance measurements. Electromyographic sensors, available as needle and surface types, facilitate EMG data acquisition, which undergoes filtering and amplification for signal processing. Integrated EMG systems and wireless EMG devices offer portability and convenience, expanding their use in clinical and diagnostic procedures. Needle EMG electrodes provide higher accuracy for nerve conduction studies, while surface EMG electrodes are preferred for rehabilitation applications.

EMG biofeedback devices aid in artifact reduction and signal interpretation, enhancing diagnostic accuracy. Clinical EMG applications include diagnostic procedures, muscle fiber conduction studies, and muscle fatigue detection. EMG signal amplification and classification are essential components of advanced EMG equipment. Th

EMG-EPN-612 Dataset

This dataset, called EMG-EPN-612, contains EMG signals of 612 people for benchmarking of hand gesture recognition systems. This dataset has been created by the Artificial Intelligence and Computer Vision Research Lab from the Escuela Politécnica Nacional, Quito-Ecuador. The data was obtained by recording, with the Myo armband, EMG signals on the forearm while users were performing five hand gestures: wave-in, wave-out, pinch, open and fist. EMGs of the hand relaxed are also included. The dataset is divided into two groups of 306 people each. One group is for training or designing hand gesture recognition models and the other is intended for testing the classification and recognition accuracy of hand gesture recognition models. In each of these two groups, each person has 50 EMGs for each of the 5 gesture recorded and also 50 EMGs for the hand relaxed.

More information about this dataset can be found at:

https://laboratorio-ia.epn.edu.ec/en/resources/dataset/2020_emg_dataset_612

This database is one of the results of the MSc dissertation of Adriano de Oliveira Andrade (http://orcid.org/0000-0002-5689-6606)

The experimental protocol is fully described in the MSc dissertation (Chapter 5 - pp. 56-60).

The avaliable program in R (Import_EMG_Files.R) can be used to import and visualize the EMG data available (EMG-DATA-MSc-AOA.zip). The EMG data are in in the folders "Isometricos" and "Isotonicos".

MSc dissertation avaiable @

[1] Andrade A de O. Metodologia para classificação de sinais EMG no controle de membros artificiais. [manuscrito]. 2000. Uberlândia: Universidade Federal de Uberlândia. doi: https://doi.org/10.13140/RG.2.2.17314.02242.

[2] http://search.ebscohost.com/login.aspx?direct=true&db=cat08055a&AN=sapere.000058379&lang=pt-br&site=eds-live

Abstract - EMG Pattern Recognition For Prosthesis Control

One of the major challenges for prosthesis development is to produce devices which mimic their natural counterparts. In general, articial limbs don't have proper feedback by which the user can assess the status of the prosthesis and the control is very unnatural. Preferably, a subconscious control is desired. Myoelectric control has been widely used as an alternative strategy designed for easier control. However, there is still a lot do be done in order to achieve articial limbs as dextrous as human limbs. In an attempt to contribute to the researches towards better artical limbs, it has been developed an EMG processing system, capable of generate input control to a four degrees of freedom prosthesis. Two major muscle groups (biceps and triceps) were used as source of electromyograc signals, which were discriminated into four different classes: elbow exion, elbow extension, wrist pronation and wrist supination. Those patterns were classied by an articial neural network, which received as inputs the EMG signal features extracted by an autoregressive model. The minimum number of pairs of electrodes and their best positioning for detection, processing and classication were also investigated. To do so, five pairs of electrodes (two on the biceps - long head (B1) and short head (B2) - and three on the triceps - long head (T1), medium head (T2) and lateral head(T3) and one pair of electrodes (on plexo brachial) configuration were considered. Isometric and isotonic contractions were analyzed for each one of those two configurations. The EMG signals were studied in several combinations for each type of contraction. The results show that the configurations using two pairs of electrodes (positioned on B2 and T1) and three pairs of electrodes (positioned on B2, T1 and T2 or B2, T1 and T3), provided accuracy as good as 100%, for the EMG pattern recognition process.

Resumo em Português - Metodologia para Classicação de Sinais EMG no Controle de Membros Artificiais

Um dos grandes desafios atuais das pesquisas envolvendo o aperfeiçoamento de membros artificiais, é que esses possam ser controlados de maneira mais natural possível pelos pacientes. Neste sentido, os processos envolvendo a aquisição e a manipulação das informações de controle provenientes do paciente, têm merecido especial atenção. Dentre as diversas técnicas de controle possíveis, uma das que tem alcançado melhores resultados utiliza a atividade eletromiográfica resultante de contrações voluntárias de determinados grupos musculares. Numa tentativa de contribuir para aquelas pesquisas, foi desenvolvido um sistema de processamento de sinais eletromiográficos (EMG), capaz de fornecer entradas de controle para uma prótese com quatro graus de liberdade. Para tal, sinais EMG provenientes dos grupos musculares tríceps e bíceps foram classificados em quatro padrões distintos: flexão e extensão do cotovelo, pronação e supinação do punho. A classificação dos padrões foi feita através de uma rede neural artifical que recebe como entrada as características dos sinais eletromiográficos, extraídas através de um modelo autoregressivo. Outro objetivo desta pesquisa foi buscar o número mínimo de pares de eletrodos e os sítios mais adequados para a detecção, processamento e classificação satisfatória dos movimentos executados. Foram feitas análises considerando 5 pares de eletrodos, sendo dois sobre o bíceps - na cabeça longa (B1) e na cabeça curta (B2) - e três sobre o tríceps - na cabeça longa (T1), na cabeça medial (T2) e na cabeça lateral (T3); e um par de eletrodos sobre o plexo braquial. Os experimentos foram realizados considerando-se contrações isométricas e isotônicas. Aqueles sinais foram analisados em diversas combinações, para cada tipo de contração, numa tentativa de se encontrar aquela que apresentasse melhores resultados. Os resultados mostraram que as combinações envolvendo o uso de dois pares de eletrodos posicionados sobre os sítios B2 e T1; e três pares de eletrodos posicionados sobre os sítios B2, T1 e T2 ou B2, T1 e T3 apresentaram melhores performances, com taxas de acerto de até 100%.

The dataset used in the EMG classification experiment. The dataset contains EMG signals from 5 subjects performing 6 grasping movements.

45°