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Attendees will experience hands-on Index demos and exhibits, listen to speakers describe how their companies are using and considering using Index AR Solutions, and network with other participants. A morning session on April 26 will give retreat participants the opportunity to walk around and interact with Index mobile apps, eBooks and experts. Breakfast will be served just outside the ballroom and participants are encouraged to eat, network and experience these best-in-class digital products. The Spring Index AR Solutions retreat will include thought-provoking presentations from industry and business leaders who will share their experience implementing Index mobile apps and eBooks.

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• Karat 711 DC
• Floor standing speakers reviews
• Using smart speakers to contactlessly monitor heart rhythms
• I bought some "Acoustic Response" speakers...
• sale outlet Peiying py-1610c Din Car Speakers 14cm-typ - 2 piece - 80 Watt sell like hot cakes
• Acoustic Research Speaker Repair Kits
• The white van speaker scam explained, and how it moved to Craigslist and Facebook
• Seeking feedback/recommendations for new speakers ($1k-$4k "budget")

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Karat 711 DC

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Heart rhythm assessment is indispensable in diagnosis and management of many cardiac conditions and to study heart rate variability in healthy individuals.

We present a proof-of-concept system for acquiring individual heart beats using smart speakers in a fully contact-free manner. Our algorithms transform the smart speaker into a short-range active sonar system and measure heart rate and inter-beat intervals R-R intervals for both regular and irregular rhythms.

We conducted a clinical study with both healthy participants and hospitalized cardiac patients with diverse structural and arrhythmic cardiac abnormalities including atrial fibrillation, flutter and congestive heart failure.

The increasing adoption of smart speakers in hospitals and homes may provide a means to realize the potential of our non-contact cardiac rhythm monitoring system for monitoring of contagious or quarantined patients, skin sensitive patients and in telemedicine settings. Clinical heart rhythm assessment depends on reliable acquisition of beat-to-beat intervals of the heart, also known as the R—R intervals.

Physiologically, the R—R interval represents the time between successive ventricular depolarizations of the heart. Acquisition and assessment of R—R interval irregularity is necessary for diagnosing many cardiac arrhythmias and to study heart rate variability HRV in healthy individuals 1 , 2.

Although frequency domain analysis can estimate average heart rate in regular and quasi-periodic heart rhythm conditions, it fails when the rhythm is irregular, which is common in pathological conditions such as atrial fibrillation 3. R—R intervals are conventionally measured by identifying individual heartbeats extracted using electrocardiography ECG.

This approach works for both regular and irregular rhythms but requires physical contact with the skin to operate. A noncontact solution for heart rhythm monitoring offers several advantages. It can monitor infectious and contagious patients where cleaning of contact-based devices can be time consuming and burdensome 4 , 5 , monitor patients in home isolation and quarantine settings, and benefit patients with skin allergies who are intolerant to wearable and contact-based devices 6.

The benefits of a self-administered test are numerous, and may include the ability to connect patients living in rural areas to physicians, screening patients for atrial fibrillation remotely, and obtaining clinical trial data without the need for an in-person visit.

The widespread adoption of high-quality smart speakers equipped with multiple microphones presents a unique opportunity for contactless monitoring of human body and internal organ functions.

Apple HomePod and Amazon Echo devices support an array of six and seven microphones, respectively, that are used for sophisticated acoustic processing 9. Here, we describe a proof-of-concept contactless system for monitoring cardiac rhythm using smart speakers that can identify individual heartbeats in both regular and irregular rhythms.

Our algorithms extract both heart rate and R—R intervals by transforming a smart speaker into a short-range active sonar system. An active sonar-based approach to contactless monitoring has the distinct benefit of scalability vis-a-vis smart speakers.

Unlike Doppler radar 10 , 11 , 12 , 13 and optical vibrocardiography 14 , 15 , 16 , active sonar hardware components i. Further, in contrast to approaches that use facial photoplethysmographic signals 17 , 18 , which raise privacy issues due to their use of cameras, active sonar can operate using inaudible acoustic signals and does not require the capturing of audible sounds. We show that a smart speaker running our algorithms that is placed in front of a subject less than a meter away can identify individual heartbeats and extract heart rate and R—R intervals for both healthy participants and patients with different cardiac abnormalities.

These data could be used for studying heart rhythms, detecting cardiac arrhythmias, and determining HRV. Prior work has focused on contactless monitoring of breathing signals using active sonar on smart devices 19 , 20 , 21 , This approach however does not work with irregular heart rhythm since there is no well-defined peak in the frequency domain and the energy is spread across a range of frequencies.

Extracting irregular beats is difficult using acoustic signals since heartbeats result in a 0. Another complicating factor is that breathing creates a much larger motion than heartbeats on the surface of the body. Though respiration rates are typically lower than heart rates, respiration is not a perfect sinusoidal motion since inhalation and exhalation durations can differ Fig.

This creates high-frequency components in the breathing motion that interfere with the minute heartbeat motion. At low signal-to-noise ratios, this prevents the latter from being reliably separated in the frequency domain using filtering Fig.

A The displacement from respiration and heartbeat in the in-phase component of the raw active sonar signal. While breathing motion blue curve is strong, the heartbeats red circle are weak and not reliably observable in this signal.

B The frequency domain with the respiratory frequency range, heart rate range, and high-frequency noise. The respiratory signal strength is much higher and its harmonics spread into heart rate frequencies preventing the latter from being reliably extracted by just filtering. C The different algorithms in our system to separate heart motion from respiration and extract individual heartbeats.

We extract individual heartbeats from reflections of these transmissions captured by a microphone array. We first preprocess the received signal at each microphone to filter out the audible frequencies to remove background noise. We then extract the impulse response of the acoustic channel which represents the times of arrival of the various reflections from the speaker to the microphone. Since cardiac motion is minute, it can be drowned out by reflections corresponding to coarse motion from distant locations.

We then separate the heart rhythm from breathing motion. Heart rhythm can be irregular, and breathing motion is not a perfect sinusoidal signal. Therefore, filtering alone is not effective.

We introduce an adaptive learning-based beamforming algorithm that maximizes the signal-to-interference and noise ratio SINR by aligning heartbeat signals across microphones and frequencies while minimizing the interference from breathing motion and noise. The adaptive beamformer uses complex weights to combine the signals from different microphones across frequencies.

To compute the weights, we formulate an optimization function that we solve using a gradient ascent algorithm Since we do not assume a priori periodic structure to the heart rhythm, the learning algorithm can erroneously detect high frequency, impulse-like signals caused by abrupt breaths or interference in the environment.

Finally, we segment the resulting heart rhythm signal into individual heartbeats. Since beamforming can be imperfect see Supplementary Fig. Our algorithm simultaneously identifies the segmenting points and the shift in each segment. We do this by 1 comparing adjacent segments to account for different segment lengths due to irregular R—R intervals and 2 tracking the shift between in-phase and quadrature-phase components caused by residual breathing motion.

Once we identify each beat segment, we compute the heart rate and R—R intervals. The acoustic waveforms include both in-phase and quadrature-phase components after beamforming.

The cardiac rhythm signal shifts between the in-phase and quadrature-phase components due to residual respiration motion that remains after beamforming. The vertical dotted lines show the segments computed by the segmentation algorithm, which combines data from both in-phase and quadrature-phase components.

The figure shows the waveforms for a A healthy participant and B atrial fibrillation patient. We recruited a cohort of 26 voluntary participants who had no prior history of cardiac conditions. The median age of the participants was 31 [interquartile range IQR , 8. The female-to-male ratio was 0. We used the ECG sensor to gather ground truth data for the study. All testing was performed in a private room at the University of Washington, where participants sat upright on a chair by a table on which our prototype smart speaker was placed.

The testing was conducted with the clothing the participants were already wearing indoors such as blouses, tops, T-shirts, and button downs made with different fabric materials. Participants took a series of 1-min measurement sessions, where they were asked to sit still and breath normally. For each healthy participant, we conducted a total of seven s sessions. Figure 3 A shows the scatter plot of the heart rates across all participants and sessions.

Measurements from the smart speaker and the ECG sensor had intraclass and concordance correlation coefficients CCCs of both 0.

Figure 3 B shows the cumulative distribution function of the error in the heart rate. The mean absolute error in the R—R intervals as a percentage of the ground truth R—R interval was 3. A Scatter plot of average heart rate in beats per minute BPM compared with ground truth. B Cumulative distribution function CDF of the absolute heart rate error.

C Scatter plot of R—R intervals compared with ground truth. Limits of agreement LOA were computed using twice the standard deviation gray dashed line in A and C. Median and 90th percentile values are noted in red circles in B and D. As the distance from the speaker to the participant increased, the acoustic signal attenuated, increasing errors.

As Fig. This demonstrates that our adaptive beamforming algorithm provided some tolerance to imperfect alignments of the smart speaker system. The algorithm is also resilient to larger angles with the smart speaker placed to the left and right of the participant; the error however is high when placed behind the participant, facing their back Supplementary Fig. Table 1 shows baseline demographic and clinical data for cardiac patients stratified by heart rhythm.

Patients in the irregular rhythm cohort were more likely to have a history of atrial fibrillation and more likely to be female. Age, BMI, reason for hospitalization, medical comorbidities, and cardiac medications were uniform between the regular and irregular rhythm cohorts. Since prior audiocardiography work showed poor results in extreme obese patients 27 , we excluded patients whose BMI exceeded 35 for this study but evaluated them in a separate study described later.

The rationale behind this method is that hospital telemetry software does not allow for digitalization and storage of the R—R interval data. Previous studies have demonstrated portable HRV devices to have acceptable error compared to gold standard ECG monitoring Ambient noise sources e. During each session, patients were instructed to remain still. All patients tolerated the data acquisition process; however, data acquisition was prematurely terminated for one patient due to developing nausea related to a prior medical condition.

Figure 5 A, B shows system performance in computing the average heart rate across all cardiac patients. The mean absolute error in the R—R intervals as a percentage of the ground truth R—R interval was 4. B The cumulative distribution function CDF of the absolute heart rate error.

Higher median R—R intervals correspond to higher 90th percentile error Supplementary Fig. There was no noticeable decrease in accuracy among those with irregular rhythms compared to those with regular rhythms. Proper diagnosis of rhythm disorders relies on the ability to detect temporally disparate R—R intervals, rather than precise R—R interval measurement.

The time series plots in Fig. Both ground truth and smart speaker data showed noticeable variation in R—R intervals, which is indicative of irregular heartbeats.

Figure 6 F shows an instance of respiratory sinus arrhythmia where both data streams showed that the R—R interval duration decreased with inspiration and increased with expiration. Figure 6 G corresponds to a patient with an implanted permanent cardiac pacemaker and a paced rhythm.

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The representative of Liberia, where a United Nations peacekeeping mission successfully completed its mandate in March, said the steep human and monetary cost of war should be enough of an incentive for countries to use their collective ingenuity and resources to invest in prevention, particularly at a time of reduced funding commitments. To drown out hatred, oppression and incitement, people must be bold and flood every space available — especially cyberspace — with a message of peace. She emphasized that peacekeeping was not an alternative to peace, and that root causes — notably occupation, inequality and exclusion — must be addressed. Funding from the Peacebuilding Commission had been invaluable, covering a range of programmes, he said, adding that Sri Lanka was an example of the need for sustained and predictable funding for the Peacebuilding Fund.

Using smart speakers to contactlessly monitor heart rhythms

Acoustic Research started manufacturing high quality, affordable loudspeakers in under the leadership of Ed Villchur. Using their patented 'acoustic-suspension' principle the company produced a small for the time bookshelf model called the AR-1 and revolutionised the speaker industry. It also made its mark on the amplifier industry as these new designs required much more powerful amplifiers than in the past. After being taken over by Teledyne the company went on to produce a range of its own separates, the AR Amp, Tuner, and Receiver under the close supervision of Roy Allison later of Allison Acoustics. AR is one of the oldest and most recognized names in audio and is responsible for many innovations that have become standards in the industry today. Acoustic Research Gallery. This website is not affiliated with or sponsored by Acoustic Research.

I bought some "Acoustic Response" speakers...

Friends of AR Turntables F. I'm new to this forum but have had an enjoyable past with home-built speakers, a DIY passive preamp, bi-amped system, and an old AR turntable that I have majorly modded. This was all done on a super-cheap budget, and I pride myself on doing things with little money, but maximum gain. Since there were no computers, getting a paper-based network going was After reading a bunch about expensive commercial mods to old ARs, I decided to attempt my own.

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Acoustic Research Speaker Repair Kits

Discussion in ' Audio Hardware ' started by dadonred , Feb 16, Log in or Sign up. Steve Hoffman Music Forums. Location: Southern US. I'm going to adopt your list of "what I seek" as my new mantra. Manimal , Feb 16, Location: Austin, TX. Manimal likes this.

The white van speaker scam explained, and how it moved to Craigslist and Facebook

Floor standing speakers AAD C C-series, produced American Acoustic Development, includes seven classic speakers - from polochnikov to giant floor towers - and six purely theatrical models. In the line of C - the smaller of the floor systems. Gracefully looks slim speakers are on the stylish support; favorably shades her strict

Seeking feedback/recommendations for new speakers ($1k-$4k "budget")

Hi, anybody knows where can i find capacitors to replace the old units in my AR9?? Those can be electrolytic or film. Film is best. Tim MusicTechnology. Find all posts by Tapetech. AR9 is classics.

Last truly visible Acoustic Research from the beginning of the Teledyne ownership and before the subsequent various ownerships and fortunes. Not that AR had been as successful in the s as it had been throughout the s but at least it had been present. The s and s turned out to be more of a disappearing act improvised in several steps The AR9 is a very vertical 59 kg four-way loudspeaker with five drivers. The two 30 cm drivers are placed on the sides of the enclosure to keep reflected sound-cancelling waves above Hz - big room necessary because that makes four 30 cm drivers outputting long waves of sound! The goal of this was to avoid the " wall dip " : where walls' reflections cancel out some of the bass output, making the transition between low mid and top of the bass range difficult and straining the amplifier's output. The filter was also designed accordingly with two ranges taken into account.

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