Posts Tagged Grip
NEOFECT announces that its NeoMano robotic glove has received the Red Dot Best of the Best award for 2019, the top prize in the Red Dot Design Concept category.
The NeoMano is a soft, lightweight glove that gives people with paralysis or limited hand function the ability to grip objects with the press of a button. A wireless, remote-controlled motor contracts the fingers of the NeoMano so wearers can perform such functions as grasping a glass of water, turning a doorknob, or maneuvering a toothbrush.
“Rehabilitation isn’t an option for everyone, including individuals living with ALS or spinal cord injuries, since in many cases they’ve permanently lost motor function,” says Scott Kim, co-founder and CEO of NEOFECT USA, headquartered in San Francisco, in a media release.
“We created the NeoMano to fit their needs, and we continue to evolve the design based on conversations and feedback from people living with these conditions. The Red Dot award recognizes our designers’ effort, dedication, and passion to helping people live fuller, more independent lives.”
The NeoMano is currently in production and will be available later this year, according to the company.
The Red Dot Awards recognize inventions, concepts, and products not yet on the market. Juries tested over 4,200 products and evaluated the entries with innovativeness, differentiation, aesthetic assessment, possibility of realization, functionality, emotionality, and value.
For more information, visit NEOFECT.
[Source(s): NEOFECT, Business Wire]
Remarkable assistive device for weak grip
Is your grip weaker than it should be due to accident, neurological condition or other illness? You can achieve a stronger grip and more power and endurance which you then can use in a very natural way with the Carbonhand.
The Carbonhand is the latest evolution of the original SEM™ Glove (Soft Extra Muscles for You) and is a smart, wearable assistive aid to improve your “grip ability” when this has been weakened by illness or trauma.
The glove mimics the human hand by using artificial tendons, motors and sensors along with some very clever software. This approach is called “mechatronics” by engineers – but what you will care about is the result – a product that can help you can have the power and endurance in your fingers to get back to a more complete life.
Developed and tested by Bioservo Technologies in Sweden, we are providing assessment, support and sales in the UK
Who Should Use it?
The Carbonhand is a medical device designed to be used by any person with a weak grip. It is important that the user is able to move their fingers into a grip and extend the fingers again otherwise the glove can’t help. People may suffer from impaired grip strength for countless reasons, such as muscle and nerve damage, muscle diseases, rheumatism and pain. The Carbonhand strengthens the grip and either compensates where power is lacking or adds extra force and endurance.
GRIP STRENGTH AND ENDURANCE IN A VERY NATURAL WAY
Every year another 60,000 UK stroke survivors will find hand and arm problems limiting their activities. With the total number of UK stroke survivors over 1 millions persons already, this is a challenge for society as a whole, as well as those affected.
When we also consider that Spinal Cord Injury, Peripheral Nerve Injury, Chronic Pain Syndrome and trauma also affect the hands of thousands, isn’t it about time we had efficient and effective aids and rehabilitation tools? And what about conditions like MS, Rheumatoid arthritis and even the effects of ageing that impact so powerfully on quality of life?
The Carbonhand consists of two main parts:
- Glove : The main purpose of the glove is to apply the forces generated by the motors in the control unit and to provide the control unit with sensory input from touch sensors at the fingertips. The forces are applied by artificial tendons that are sewn into the glove along the length of the fingers.
- Control unit : The control unit contains a rechargeable battery power source, one motor for each finger which receives extra force and a micro-controller that controls the SEM™ Glove’s functionality.
Who Should Use it?
The Carbonhand is a medical device designed to be used by any person with a weak grip. People may suffer from impaired grip strength for countless reasons, such as muscle and nerve damage, muscle diseases, rheumatism and pain. The product strengthens the grip and either compensates where power is lacking or adds extra force and endurance.
Who Can’t Use it?
The main reason that the product would be ineffective is a complete paralysis of the hand. The sensors in the fingers respond to the user’s intention and ability to apply pressure to the object being gripped. If the person can’t use the fingers at all, the device cannot sense the users intention.
How Do I Try it?
We first must assess if the device is suitable for you. If it is, we will be able to adjust the settings so they suit your current grip issues. You will wear a snugly fitting glove on your affected hand. The thumb and two fingers have pressure sensors in the tips that are essential to the glove’s function. A cable bundle connects the glove to a control pack that sits, for example, on your belt. Rechargeable batteries deliver around 8 hours use. Because the sensors in the glove operate based on touch pressure, you can wear another protective glove over the Carbonhand if necessary for, let’s say, a particular work situation.
UK Pricing is based on a Euro exchange rate with a system package of a control unit, appropriate size glove, batteries, battery charger and manual currently costing around £6,000. As the price will vary with the exchange rate please check with us for accurate price information.
All UK potential clients will be asked to complete the PRE ASSESSMENT Form here
[ARTICLE] Classification of EEG signals for wrist and grip movements using echo state network – Full Text
Brain-Computer Interface (BCI) is a multi-disciplinary emerging technology being used in medical diagnosis and rehabilitation. In this paper, different techniques of classification and feature extraction are applied to analyse and differentiate the wrist and grip flexion and extension for synchronized stimulation using sensory feedback in neuro-rehabilitation of paralyzed persons. We have used an optimized version of Echo State Network (ESN) to identify as well as differentiate the wrist and grip movements. In this work, the classification accuracy obtained is greater than 96% in a single trial and 93% in discrimination of four movements in real and imagination.
The popularity of analysing brain rhythms and its applications in healthcare is evident in rehabilitation engineering. Motor disabilities as a consequence of stroke require rehabilitation process to regain the motor learning and retrieval. The classification of EEG signals obtained by using a low cost Brain Computer Interface (BCI) for wrist and grip movements is used for recovery. Using Movement Related Cortical Potential (MRCP) associated with imaginary movement as detected by the BCI, an external device can be synchronized to provide sensory feedback from electrical stimulation . The timely detection, classification of movement and the real time triggering of the electrical stimulation as a function of brain activity is desirable for neuro-rehabilitation [2,3]. Thus, BCI has an active role in helping out the paralyzed persons who are not able to move their hand or leg . Using BCI system, EEG data is recorded and processed. The acquired data should have the least component of environmental noise and artifacts for effective classification . EEG signals acquired from the invasive method are found to exhibit least noise components and higher amplitude. However, in most applications, a non-invasive method is preferred. The human brain contains a number of neuron networks. EEG provides a measurement of brain activity as voltage fluctuations which are recorded as a result of ionic current within neurons present inside the brain . Many people have motor disabilities due to the nerve system breakdown or accidental failure of nerve system. There are different methods to resolve this problem, e.g. neuro-prosthetics (neural prosthetics) and BCI [3,7–9]. In neuro-prosthetics, a solution of the problem is in the form of connecting brain nerve system with the device and in BCI connecting brain nerve system with computer . BCI produce a communication between brain and computer via EEG, ECOG or MEG signals. These signals contain information of any of our body activity . Moreover, in addition to neuro-rehabilitation, assistive robotics and brain control mobile robots also utilizes similar technologies as reported recently [11,12]. The signal processing of these low amplitude and noisy EEG signals require special care during data acquisition and filtering. After recording EEG measurements, these signals are processed via filtration, feature extraction, and classification. Simple first or second order Chebyshev or Butterworth filter can be used as a low pass, high pass or a notch filter. Some features can be extracted by using one of the techniques from time analysis, frequency analysis, time-frequency analysis or time-space-frequency analysis [13,14]. Extracted EEG signal further classify by using one of the techniques like LDA, QDA, SVM, KNN etc. [15,16].
We aim to classify the wrist and grip movements using EEG signals. This research will be helpful for convalescence of persons having disabilities in wrist or grip. Our work is based on offline data-sets, in which the EEG data is collected multiple times from 4 subjects. We present the following major contributions in this paper: First, the differentiation between the wrist and grip movements has been performed by using imaginary data as well as the real movements. Secondly, we have tested multiple algorithms for feature extraction and classification and used ESN with optimized parameters for best results. This paper is organized as follows: section 2 describes a low-cost BCI setup for EEG, section 3 deals with the DAQ protocol, section 4 explains the echo state network and its optimization while section 5 discusses results obtained in this research. Section 6 concludes the paper.
Brain Computer Interface Design
Brain-Computer Interface (BCI) design requires a multi-disciplinary approach for engineers to observe EEG data. Today, a number of sensing platforms are available which provide a low-cost solution for high-resolution data acquisition. Developing a BCI interface requires a two-step approach namely the acquisition and the real-time processing. In off-line processing, the only requirement is to do the acquisition. The data is acquired via a wireless network from the pick-off electrodes arranged on the scalp of the subjects . One such available system is Emotiv, which is easy to install and use. Emotiv headset with 14 electrodes and 2 reference electrodes, CMD and DRL, is used to collect data as shown in Figure 1. All electrodes have potential with respect to the reference electrode. Emotiv headset is a non-invasive device to collect the EEG data as preferred in most of the diagnosis and rehabilitation applications .
It is important to understand the EEG signal format and frequency content for pre-processing and offline classification. Table 1 shows some of the indications of physical movements and mind actions associated with different brain rhythms in somewhat overlapping frequency bands. It is obvious that the motor imagery tasks are associated with the μ-rhythm in 8-13 Hz frequency band .
|Δ||0-4||Deep sleep stage||Hypoglycaemia, Epilepsy|
|υ||4-7||Initial sleep stage||–|
|α||8-12||Closure of eyes||Migraine, Dementia|
|β||12-30||Busy/Anxious thinking||Encephalopathies, Tonic seizures|
|µ||8-13||Motor imagery tasks||Autism Spectrum Disorder|
Table 1. Brain frequency bands and their significance.
[ARTICLE] Comparison of Grip and Pinch Strength in Adults with Dexterity Limitations to Normative Values – Full Text PDF
Upper extremity function, which includes manual dexterity, plays an important role in one’s ability to perform activities of daily living. Dexterity may be reduced for a variety of reasons such as age, injury or disease. Understanding the capabilities of these users is critical to the effective design of products to meet their needs. While normative dexterity data is available for healthy adults, none has been compiled for people with dexterity limitations. The purpose of this study was to measure grip strength, key pinch strength and tip pinch strength in users with limited dexterity and compare them to adult norms. Average strength values were lower in all tests in men and women. It was observed that the dominant hand was strongest in each test in women but not always in men. No strong correlation between age and hand strength was observed. The sample size in this study was small so specific results are hard to generalize. The emergences of clear trends that differ from healthy norms indicate that a wider study may be fruitful.