Altec Inc.

a sister company of Delsys

A Commitment to Forward Thinking

Altec is grounded in Research and Innovation for the purpose of advancing bio-signal and wearable sensor technologies used in movement sciences. We draw on engineering and physiology principles to design advanced ground-breaking technologies that provide ease-of-use for the customer and reveal new information contained within bio-signals, particularly from the electromyographic (EMG) signal.

Our ability to successfully transfer innovations into commercial products was recognized by achieving a Top 40 NASA Start-up Company designation [NASA Spinoff 2000] and receipt of the 2006 Tibbetts Award from the Small Business Technology Council for “exemplifying the best in the Small Business Innovation Research program”.

Our advanced research work has been funded by: Private Corporations, DARPA, NASA, and the National Institutes of Health (specifically NINDS, NIAMS, NCMRR, NIEHS, and NIDCD).

The following are samples of our forward-thinking concepts.

dEMG Technology for Extracting Information from the EMG Signal

dEMG Technology for Extracting Information from the EMG Signal

The underlying neural mechanisms that regulate how muscles generate and control force are embedded in the characteristics of electrical pulses that originate in the brain and are transmitted to the muscle fibers by nerves. These pulses, known as motor unit action potentials (MUAPTs), can be detected on the surface of a muscle, but only after they superimpose into a complex signal known as the EMG signal. If it were possible to extract the individual MUAPTs, the information contained in their firing behavior would enable us to understand the underlying neural mechanisms and how they are impaired by injury and neuromuscular diseases.

For the past eight decades, the individual MUAPTs could only be detected with invasive and painful methods that yielded only a few examples of the rich electrical activity within the muscle, providing limited and unreliable data.

We have been developing a dEMG technology described in Delsys dEMG website to provide a non-invasive, high-yield, and fully automated means of extracting the MUAPs from the detected EMG signal. The technology uses a small specially designed array sensor and a proprietary algorithm that decomposes the surface EMG signal into the individual MUAPTs. The advancement represents an important break-through that is far superior to other competitive attempts by other groups. A discussion on the superiority of our dEMG technology may be found in Delsys Blog Article.

The applications of this technology are stunning. Imagine designing studies that reveal the control schemes used by the Central Nervous System to generate and control force production. Do the control schemes alter during fatigue?  Imagine identifying modifications in the firing characteristics of muscle fibers in diseased and dysfunctional muscles – in stroke, in Parkinson, in ALS, etc. Imagine using the information content in the firing behavior of muscle fibers to provide precise control of artificial prostheses.

For a demonstration of how to use our dEMG technology go to Delsys Webinar and see the surprising firing behavior of muscle fibers during fatigue.

The present version has been described extensively in the peer-reviewed literature. Independent researchers from other institution have verified its accuracy.  However, it is constrained to work on EMG signals obtained during isometric (constant length) muscle contractions.  An evolving version is revealing promising results on dynamic cyclic muscle contractions. In Publication list below.

Applications:

• Motor Control

• Ageing Studies

• Prosthetics

• Muscle Fatigue

• Muscle Force Modelling

Publications:

  1. Hu X, Rymer WZ, Suresh NL, Accuracy assessment of a surface electromyogram decomposition system in human first dorsal interosseus muscle. Journal of Neural Engineering, 11(2), 2014. PMID: 24556614. (pdf)
  2. Hu X, Rymer WZ, Suresh NL. Assessment of validity of A High-Yield Surface Electromyogram Decomposition. J Neuroeng Rehabil 23;10(1):99. 2013. PMID: 24059856. (pdf)
  3. Hu X, Rymer WZ, Suresh NL. Motor Unit Pool Organization Examined via Spike Triggered Averaging of the Surface Electromyogram. J Neurophysiol  110(5):1205-20. 2013. PMID: 23699053. (pdf)
  4. Hu X, Rymer WZ, Suresh NL. Reliability of spike triggered averaging of the surface electromyogram for motor unit action potential estimation. Muscle Nerve 48(4): 557-70, 2013. PMID: 23424086. (pdf)
  1. De Luca CJ, Nawab SH, Kline JC. Clarification of methods used to validate surface EMG decomposition algorithms as described by Farina et al. (2014). J Appl Physiol 118(8), 2015. PMID: 25878218. (pdf)
  2. De Luca CJ, Chang SS, Roy SH, Kline JC, and Nawab SH. Decomposition of Surface EMG Signals from Cyclic Dynamic Contractions. J Neurophysiol 113(6):1941-51, Mar 2015. PMID: 25540220.  (pdf)
  3. Kline JC and De Luca CJ. Error Reduction in EMG Signal Decomposition. Journal of Neurophysiology. 112(11):2718-28, 2013. PMID: 25210159. (pdf)
  4. De Luca CJ, Roy S, Chang SS. Tracking Motor Unit Firings during Dynamic Cyclic Contractions. The 7th World Congress of Biomechanics, Boston, July 2014. (pdf)
  5. De Luca CJ and Nawab SH. Reply to Farina and Enoka: The Reconstruct-and-Test Approach Is the Most Appropriate Validation for Surface EMG Signal Decomposition to Date. Journal of Neurophysiology, 105: 983-984, 2011. (pdf)
  6. Nawab SH, Chang SS, and De Luca CJ. High-yield decomposition of surface EMG signals. Clinical Neurophysiology, 121(10):1602-1615, 2010.PMID: 20430694. (pdf)
  7. Nawab SH, Chang SS, and De Luca CJ. Surface EMG Signal Decomposition Using Empirically Sustainable BioSignal Separation Principles. The 31st Annual International Conference of the IEEE EMBS, Minneapolis, September 2009. PMID: 19964658. (pdf)
  8. Chang SS, De Luca CJ, and Nawab SH. Aliasing Rejection in Precision Decomposition of EMG Signals. The 30th Annual International Conference of the IEEE EMBS, Vancouver, August 2008. PMID: 19163833. (pdf)
  9. De Luca CJ, Adam A, Wotiz R, Gilmore LD, and Nawab SH. Decomposition of surface EMG signals. Journal of Neurophysiology, 96: 1646-1657, 2006. (pdf)
  1. Contessa P, Puleo A, De Luca CJ. Is the notion of central fatigue based on a solid foundation? J Neurophysiol February 2016 115:967-977. PMID: 26655823 (pdf)
  2. De Luca CJ, Kline JC. The common input notion, conceived and sustained by conjecture. J Neurophysiol February 2016 115:1079-1080. PMID: 26905085 (pdf)
  3. Herda TJ, Miller JD, Trevino MA, Mosier EM, Gallagher PM, Fry AC, Vardiman JP. The change in motor unit firing rates at derecruitment relative to recruitment is correlated with type I myosin heavy chain isoform content of the vastus lateralis in vivo. Acta Physiol (Oxf) Oct. 2015. PMID: 26513624. (pdf)
  4. Kline JC, De Luca CJ. Synchronization of Motor Unit Firings: An Epiphenomenon of Firing Rate Characteristics Not Common Inputs. J Neurophysiol. Oct 2015. PMID: 26490288. (pdf)
  5. Ye X, Beck TW, Wages NP. Influence of prolonged static stretching on motor unit firing properties. Muscle Nerve. Sept 2015. PMID: 26378724. (pdf)
  6. Hu X, Suresh AK, Rymer WZ, Suresh NL. Assessing altered motor unit recruitment patterns in paretic muscles of stroke survivors using surface electromyography. J Neural Eng. 12(6). Sept. 2015 PMID: 26402920. (pdf)
  7. McManus L, Hu X, Rymer WZ, Lowery MM, Suresh NL. Changes in motor unit behavior following isometric fatigue of the first dorsal interosseous muscle. J Neurophysiol 113(9):3186-96. May, 2015. PMID: 25761952. (pdf)
  8. De Luca CJ, Contessa P. Biomechanical Benefits of the Onion-skin Motor Unit Control Scheme. Journal of Biomechanics, 48(2), Jan 2015. PMID: 25527890. (pdf)
  9. Stock MS, Thompson BJ. Effects of Barbell Deadlift Training on Submaximal Motor Unit Firing Rates for the Vastus Lateralis and Rectus Femoris. PLoS ONE 9(12), Dec 2014. PMID: 25531294. (pdf)
  10. Hu X, Rymer WZ, Suresh NL. Control of motor unit firing during step-like increases in voluntary force. Front. Hum. Neurosci. 8:721, 2014. PMID: 25309395. (pdf)
  11. Trevino MA, Herda TJ, Cooper MA. The effects of poliomyelitis on motor unit behavior during repetitive muscle actions: a case report. BMC Research Notes 2014, 7:611. PMID: 25194883. (pdf)
  12. De Luca CJ and Kline JC. Statistically rigorous calculations do not support Common Input and Long-Term synchronization of motor unit firings. Journal of Neurophysiology. 112(11). Dec 2014. PMID: 25210152. (pdf)
  13. De Luca CJ, Kline JC, Contessa P. Transposed firing activation of motor units. J Neurophysiol, 12:962-970, Aug, 2014. PMID: 24899671. (pdf)
  14. Defreitas JM, Beck TW, Ye X, Stock MS. Synchronization of low- and high-threshold motor units. Muscle Nerve Jul 28, 2013. PMID: 23893653. (pdf)
  15. Zaheer F, Roy SH, De Luca CJ. Preferred Sensor Sites for Surface EMG Signal Detection. Physiological Measurement, 33(2), 2012. PMID: 22260842. (pdf)
  16. De Luca CJ and Contessa P. Hierarchical Control of Motor Units in Voluntary Contractions. Journal of Neurophysiology, 107(1): 178-195, 2012. PMID: 21975447. (pdf)
  17. De Luca CJ and Kline JC. Influence of proprioceptive feedback on the firing rate and recruitment of motorneurons. Journal of Neural Engineering, 9(1):016007, 2012. PMID: 22183300. (pdf)
  18. Stock WS, Beck TW, Defreitas JM. Effects of Fatigue on Motor Unit Firing Rate versus Recruitment Threshold RelationshipMuscle & Nerve, 45: 100-109, 2012. PMID: 22190315. (pdf)
  19. Beck TW, Kasishke PR 2nd, Stock MS, Defreitas JM. Eccentric exercise does not affect common drive in the biceps brachii. Muscle & Nerve, 46: 759-66, 2012. PMID: 22941727. (pdf)
  20. Beck TW, Stock MS, Defreitas JM. Effects of fatigue on intermuscular common drive to the quadriceps femoris. Int J Neurosci 122(10): 574-82, 2012. PMID: 22591395. (pdf)
  21. Hu X, Suresh AK, Li X, Rymer WZ, Suresh NL. Impaired motor unit control in paretic muscle post stroke assessed using surface electromyography: a preliminary report. Conf Proc IEEE Eng Med Biol Soc 2012: 4116-9, 2012. PMID: 23366833. (pdf)
  22. Suresh N, Li X, Zhou P, Rymer WZ. Examination of Motor Unit Control Properties in Stroke Survivors Using Surface EMG Decomposition: A Preliminary Report. The 33rd Annual International Conference of the IEEE EMBS, Boston, September 2011. PMID: 22256256. (pdf)
  23. Richards J, Selfe J. EMG Decomposition of Vastus Medialis and Vastus Lateralis in normal subjects and patellofemoral patients: A new way of assessing the balance of muscle function? International Patellofemoral Research Retreat, Ghent 31 August – September 2011. (pdf)
  24. Beck TW, DeFreitas JM, Stock MS, Dillon MA. Effects of resistance training on force steadiness and common drive. Muscle & Nerve, 43(2) 245-250, 2011. PMID: 21254090. (pdf)
  25. Beck TW, DeFreitas JM, Stock MS. The Effects of a Resistance Training Program on Average Motor Unit Firing Rates. Clinical Kinesiology, 65(1), 2011. (pdf)
  26. De Luca CJ, Hostage EC.  Relationship between firing rate and recruitment threshold of motoneurons in voluntary isometric contractions.  Journal of Neurophysiology, 104: 1034-1046, 2010. PMID: 20554838. (pdf)

Reassessment of Widely Accepted Motor Control Notions

Our dEMG technology has provided us with the means to query the validity of commonly accepted notions of motor unit control. A brief summary of our work follows, with reference to relevant published work listed in Publication list below.

Muscles are not controlled to maximize muscle force production  —  For the past six decades it has been widely promulgated that motor units are controlled to maximize muscle force production. This notion was based on the long-standing belief that earlier recruited motor units fire at lower firing rates than later recruited ones. This construct would enable all motor units to fire at the rate needed to produce their maximal force output. However, if this were so, the later recruited higher-firing rate motor units would fatigue quickly and the contraction could not be sustained for a prolonged time.

By applying our dEMG technology we have now shown conclusively that the opposite firing rate construct is true. That is, earlier recruited motor units have greater firing rates than later recruited ones at any force level and at any time during voluntary contractions. Consequently, motor units are not controlled to maximize muscle force. Instead, they appear to be controlled to balance a combination of force magnitude and contraction duration, a construct that is more conducive to evolutionary survival.  

Synchronization is not caused by Common Inputs to Motor Units  —  For over nine decades and continuing to the present day, there has been a communal acceptance of the notion that common inputs from the central and peripheral nervous system to the motor units that comprise a muscle are responsible for causing some of the motor unit firings to synchronize. That is, they display the tendency to fire in the near proximity of each other. This was originally postulated as a conjecture and remains a conjecture to this day. No empirical evidence has ever been put forth to substantiate this point.  With the assistance of a newly developed, statistically robust methodology for measuring synchronization we have recently provided empirical evidence indicating that the common inputs do not explain the observable behavior of synchronization.

Does muscle fatigue originate in the muscle or in the brain? – Exercise-induced muscle fatigue is manifested as the reduced capacity of muscles to produce force. There is evidence that fatigue is, in large part, due to peripheral factors that impair the muscle fiber contractile mechanisms. However, for over a century factors arising within the Central Nervous System have also been hypothesized to induce muscle fatigue, but no direct empirical evidence has yet been reported. We developed a simulation model to investigate whether peripheral factors of muscle fatigue are sufficient to explain the muscle force behavior observed during empirical studies of fatiguing voluntary contractions. We found that the force behavior commonly attributed to Central Fatigue could be explained solely by peripheral factors. It also revealed important flaw in the methods currently used for assessing Central Fatigue.

Transposition of Motor Unit Firings  —  It has been generally held that as the contraction force increases new motor units are recruited and the firing rate of active motor units simultaneously increases; with the inverse occurring as the contraction force decreases. This behavior is true for slowly varying contractions. With our dEMG technology we are able to study the behavior of motor unit firings during rapid cyclic contractions wherein we see a dramatically different firing behavior. Depending on the frequency of the cyclic contraction, it is possible to simultaneously recruit higher threshold motor units and decrease the firing rate of active lower threshold motor units. This revolutionary discovery holds promise for designing novel exercise programs that separately target relatively low-threshold motor units and relatively higher-threshold motor units.

Publications

  1. Kline JC, De Luca CJ. Synchronization of Motor Unit Firings: An Epiphenomenon of Firing Rate Characteristics Not Common Inputs. J Neurophysiol. Oct 2015. PMID: 26490288. (pdf)
  2. De Luca CJ, Contessa P. Biomechanical Benefits of the Onion-skin Motor Unit Control Scheme. Journal of Biomechanics, 48(2), Jan 2015. PMID: 25527890. (pdf)
  3. De Luca CJ and Kline JC. Statistically rigorous calculations do not support Common Input and Long-Term synchronization of motor unit firings. Journal of Neurophysiology. 112(11). Dec 2014. PMID: 25210152. (pdf)
  4. De Luca CJ and Contessa P. Hierarchical Control of Motor Units in Voluntary Contractions. Journal of Neurophysiology, 107(1): 178-195, 2012. PMID: 21975447. (pdf)
  5. De Luca CJ and Kline JC. Influence of proprioceptive feedback on the firing rate and recruitment of motorneurons. Journal of Neural Engineering, 9(1):016007, 2012. PMID: 22183300. (pdf)
  6. De Luca CJ, Hostage EC.  Relationship between firing rate and recruitment threshold of motoneurons in voluntary isometric contractions.  Journal of Neurophysiology, 104: 1034-1046, 2010. PMID: 20554838. (pdf)

Movement Disorder Monitor

Movement Disorder Monitor



Forty-five million people in the US suffer from an involuntary movement disorder. Accurate motor symptom tracking is crucial to improve therapy for those suffering from Parkinson’s disease, stroke, essential tremor, ALS, cerebral palsy, and other disorders. However, monitoring these symptoms using the current standard of paper-and-pencil instruments, or visual observation, is impractical.

We have developed a generic software platform to monitor how the presence and severity of movement disorders change with treatment or as the disease progresses using only wearable sensor data. Our system is designed to use a minimum number of hybrid sensors that are capable of simultaneously detecting muscle activation and movement data. The application software works with our ambulatory data acquisition systems to create continuous symptom tracking devices for Parkinson disease that can be currently used in a lab or clinical environment.

Find detailed descriptions in the references listed in Publication list below. Future work will expand the technology to track other neurological conditions, and develop a version for unsupervised home use.

Applications:

• Limb-specific Tracking of Disorder Severity

• Continuous Tracking during Daily Activity

• Muscle Activity and Movement Sensors

• Objective and Accurate Summary Reports

• Multiple Disorders

Publications:

  1. Cole BT, Roy SH, De Luca CJ, and Nawab SH. Dynamical Learning and Tracking of Tremor and Dyskinesia from Wearable Sensors, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22 (5): 982-91, Sept 2014. PMID 24760943. (pdf)
  2. Roy SH, Cole BT, Gilmore LD, De Luca CJ, Thomas CA, Saint-Hilaire MM and Nawab SH. High-resolution tracking of motor disorders in Parkinson’s disease during unconstrained activity. Movement Disorders,28(8):1080-87, July 2013. PMID: 23520058. (pdf)
  3. Roy SH, Cheng MS, Chang SS, Moore J, De Luca G, Nawab SH, and De Luca CJ.  A combined sEMG and accelerometer system for monitoring functional activity in strokeIEEE Transactions on Neural Systems and Rehabilitation Engineering, 17 (6): 585-94, 2009. PMID: 20071273. (pdf)

System for Non-vocal Speech Communication

System for Non-vocal Speech Communication

Some patients with speech deficits, such as individuals with laryngectomy or brain injury, cannot vocalize. Others, such as Special Forces soldiers, may need to rely on voiceless communication for covert operations or noisy environments. Imagine a wearable technology that gives voice to the voiceless, or provides silent communication.

We are collaborating with researchers from BAE Systems, the Massachusetts General Hospital Voice Center and Northeastern University (Boston, USA) to develop a non-vocal speech communication device that translates facial muscle signals produced when mouthing words into automated speech. Our preliminary results are detailed in Publication list below.

Current algorithms can process continuous speech from unimpaired and speech dysfunction subjects with less than 10% error rate. Improvements in the detection technology and in the signal processing algorithms hold the promise of rendering a practical implementation with a reduced error rate.

 “This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. D13PC00074. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA); or its Contracting Agent, the U.S. Department of the Interior, National Business Center, Acquisition Services Directorate, Sierra Vista Branch.”

Publications:

  1. Deng Y, Patel R, Heaton JT, Colby G, Gilmore LD, Cabrera J, Roy SH, De Luca CJ, Meltzner GS. Disordered Speech Recognition Using Acoustic and sEMG Signals, Interspeech, Brighton UK, Sept 2009. (pdf)
  2. Colby G, Heaton JT, Gilmore LD, Sroka J, Bend Y, Cabrera J, Roy SH, De Luca CJ, Meltzner GS. Sensor Subset Selection for Surface Electromyography Based Speech Recognition, Proc. IEEE Int. Conf. Acoustics Speech and Signal Processing (ICASSP), 2009, Taipei, Taiwan. (pdf)
  3. Meltzner GS, Sroka J, Heaton JT, Gilmore LD, Colby G, Roy SH, Chen N, and De Luca CJ. Speech Recognition for Vocalized and Subvocal Modes of Production Using Surface EMG Signals from the Neck and Face, Interspeech 2008, Brisbane, Australia, Sept 2008. (pdf)

Sensor Design and User Guidelines

Sensor Design

Over the past three decades we have continuously expressed concern regarding the insufficient attention given by manufacturers and users to the manner in which the surface EMG signal is detected, and more importantly, how it is analyzed and interpreted to draw inferences about the behavior of the muscles. All sensor technology introduces noise components to the EMG signal. All recordings of the EMG signal are contaminated with noise. It is important that the user understand these contaminations before they draw conclusions about the activity of the muscle from which the signal is detected. This is a critically important issue. To assist the user in appreciating this pervasive problem we have prepared a webinar that highlights these concerns and provides suggestions based on empirical research findings on how to mitigate noise contaminations. Furthermore, we provide surface EMG signals that monitors the recorded signal in real-time and alerts the user of any excessive noise issues.

We have strived over the past two decades to provide the EMG user with the best possible sensor technology. Our aims have been to simplify the use of the sensors, to provide stability in the recorded signal, and to minimize the influence of contaminant factors, such as ambient noise, motion artifact and cross-talk from adjacent muscles.  Our designs are grounded in realistic, factual, empirical measurements that have been published in the peer-reviewed literature, wherein we explain the reasoning for our designs for all to appreciate. See references in Publication list below.

Our sensors are designed with a patented fixed 1 cm inter-electrode spacing. This spacing ensures signal stability and reduces the contaminating cross-talk signal from adjacent muscles. It also allows recording of EMG signals from small muscles, such as those of the hand, forearm, face, and neck, among other locations. We recommend a low-frequency cut-off of 20 Hz for the EMG signal. This cut-off is based on the empirical observation that the frequency range between 5 and 20 Hz contains minimal (about 4%) EMG signal energy, while most of the energy derives from movement artifact. The circuitry and design of our sensors are engineered to reduce contaminating noise and provide a robust sensor-skin interface.

Publications:

  1. Zaheer F, Roy SH, De Luca CJ. Preferred sensor sites for surface EMG signal detection. Physiological Measurement, 33(2):195-206, 2012. PMID: 22260842. (pdf)
  2. De Luca CJ, Kuznetsov M, Gilmore LD and Roy SH.  Inter-electrode spacing of surface EMG sensors: Reduction of crosstalk contamination during voluntary contractions. Journal of Biomechanics,45(3):  555-561, 2011. PMID: 22169134. (pdf)
  3. De Luca CJ, Gilmore LD, Kuznetsov M, and Roy SH.  Filtering the Surface EMG signal: Movement artifact and baseline noise contaminationJournal of Biomechanics, 43 (8): 1573- 1579, 2010. PMID: 20206934. (pdf)
  4. Roy SH, De Luca G, Cheng S, Johansson A, Gilmore LD, and De Luca CJ. Electro-Mechanical stability of surface EMG sensorsMedical & Biological Engineering & Computing, 45: 447-457, 2007. PMID: 17458582. (pdf)
  5. De Luca CJ and Merletti R. Surface myoelectric crosstalk among muscles of the leg. Electroencephalography and Clinical Neurophysiology, 69: 568-575, 1988. PMID: 2453334. (pdf)
  6. Roy SH, De Luca CJ, and Schneider J. Effects of electrode location on myoelectric conduction velocity and median frequency estimates. Journal of Applied Physiology, 61: 1510-1517, 1986. PMID: 3781964. (pdf)

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