\n\tMake sure the AirPods are connected to the case. \n\n\nWhen you insert your AirPods for charging, do both of them attach to the case? When you put your AirPods into the charging case, you should hear a magnetic snap, indicating that they are correctly attached. Remove the AirPods to examine whether debris or filth is keeping them from sitting correctly in the case if your AirPods aren't attaching properly or the top of your AirPods case won't close. If this is the case, cleaning your AirPods and the inside of your charging case will most likely fix your charging problems.\n\n\n\tYour AirPods need to be charged.\n\n\nPlace your AirPods work in the charging case and close the lid if you have AirPods or AirPods Pro. Using the wire that came with your AirPods, charge your AirPods and case for at least 15 minutes.\n\nCharge your AirPods Max for at least 5 minutes with the wire that came with them if you have them. Keep your AirPods Max in the Smart Case when not in use to put them in ultra-low power mode and preserve battery life.\n\nIf you're using a Qi-compatible charger and have the Wireless Charging Case for your AirPods or AirPods Pro, make sure the status light is facing up. While continuing, the status light should turn on for a few seconds before turning off.\n\n\n\tReset your AirPods to factory defaults.\n\n\nIf you've done everything else and your AirPods are not charging, they may have a problem that can be fixed with a factory reset. Unfortunately, you'll have to re-pair your AirPods, which will wipe away any previously saved settings, so do so only if nothing else has worked.\n\nIn the case, place your AirPods. Toggle on Bluetooth in the Settings app. Then hit the I to the right of the entry for your AirPods. Confirm that you want to forget this device by tapping Forget this Device. After that, you can reopen the cover and reconnect your AirPods. To start the setup if it isn't automatic, press and hold the button on the back of the casing.\n\nWhen Everything Else Fails\n\nIf you've exhausted all other options and still can't get your AirPods or AirPod case to charge, you can replace either the AirPods or the charging case. If your AirPods are still covered by the manufacturer's warranty, Apple may be able to repair the broken part. If your AirPods are no longer under warranty, you can try PodSwap, a service that replaces deceased AirPods.\n\nHow Do I Set Up AirPods Using My iPhone?\n\n\n\tUnlock your iPhone.\n\tOpen the Charging Case with your AirPods inside.\n\tHold the Charging Case next to your iPhone.\n\tA setup animation will appear on your iPhone.\n\tTap Connect.\n\tTap Done.\n
BACKGROUND: The recent U.S. Food and Drug Administration authorization of AirPods Pro as over-the-counter hearing aids (HAs) has increased interest in consumer devices as potential alternatives to traditional amplification; however, their electroacoustic performance relative to clinically fitted HAs remains unclear. The purpose of this study was to compare the electroacoustic characteristics and real-ear measures of AirPods Pro 2nd generation (APP2), AirPods Pro 3rd generation (APP3), and a traditional receiver-in-the-canal HA across mild flat, mild-to-moderate sloping, and moderate flat hearing loss configurations. METHODS: Outcome measures included 2cc coupler output curves, saturation sound pressure level for a 90 dB input (SSPL90), real-ear speech mapping, maximum power output (MPO), and real-ear-to-coupler differences. RESULTS: Coupler-based electroacoustic measures showed that APP2 and APP3 produced output comparable to the traditional HA (within 7 dB). SSPL90 outputs were similar for APP2 and APP3, whereas the HA demonstrated profile-dependent increases. In contrast, real-ear measurements demonstrated that both APP2 and APP3 consistently produced less output relative to the HA that was fitted to NAL-NL2 targets, with the largest deviations observed for moderate hearing loss and at higher frequencies (up to 14 dB). Across all configurations, MPO was consistently highest for the HA, with both AirPods devices exhibiting reduced maximum output, especially in speech-critical frequency regions. Real-ear-to-coupler difference findings indicated reduced acoustic coupling for APP3 relative to APP2 and the HA, contributing to reduced in-ear amplification despite comparable coupler outputs. CONCLUSIONS: While AirPods Pro may offer benefit for mild hearing loss or moderate high-frequency hearing loss, they do not provide output comparable to prescriptively fitted HAs. These findings underscore the continued importance of clinical verification and prescription-based fitting of hearing assistive technology for achieving appropriate audibility across hearing loss configurations.
PURPOSE: This study aimed to assess the feasibility of the Apple AirPods Pro with the headphone accommodation feature as a hearing assistive device for patients with mild to moderate hearing loss (HL). MATERIALS AND METHODS: The study included a total of 35 participants with mild to moderate HL. To determine the degree of HL in the participants, a screening test using pure-tone audiometry was conducted prior to the main tests of functional gain, word recognition score (WRS), and sentence recognition in noisy environments. The study employed two hearing devices: the Bean (a personal sound amplification product, PSAP) and the AirPods Pro. RESULTS: Regarding functional gain, there were no significant differences between the Bean and the AirPods Pro at all frequencies, except 8 kHz. In terms of WRS, both the Bean and the AirPods Pro had higher scores than the unaided condition. In sentence recognition, both the Bean and the AirPods Pro had higher scores than the unaided condition. During real-ear measurement, the Bean demonstrated consistent frequency responses, while the AirPods had a deviation exceeding 10 dB SPL at 6 kHz in the left ear. This deviation was absent for all other frequencies. CONCLUSION: This study shows that the Apple AirPods Pro, with its headphone accommodation feature, performed similarly to a validated PSAP and improved hearing compared to unaided conditions.
Purpose: The purpose of this study was to evaluate the benefit of the Apple AirPods Pro 2 Live Listen feature on speech recognition and recall in noise among older adults in a controlled laboratory setting. Method: Twenty adults aged 60–90+ years completed a modified speech-in-noise task with and without AirPods using the Live Listen feature. A Quick Speech-in-Noise Test–derived task measured recognition and recall under both conditions. Participants also completed audiometric screening, working memory assessment, and signal-to-noise ratio (SNR) loss estimation. Paired t tests and multiple regression analyses were used to assess performance differences and predictors. Results: Participants demonstrated significantly higher speech recognition ( p < .00001) and recall ( p = .00023) when using Live Listen. Recognition improvements were predicted by age, SNR loss, and sex, while recall improvements showed no significant predictors. Conclusions: The Live Listen feature of the Apple AirPods Pro 2 significantly improved speech-in-noise performance in older adults. These preliminary findings support its potential as an affordable assistive tool in noisy environments, such as hospitals, where traditional hearing aids may be unavailable.
Objective To investigate the extent to which Headphone Accommodations in Apple AirPods Pro attend to the hearing needs of individuals with normal audiograms who experience hearing difficulties in noisy environments.Design Single-arm interventional study using acoustic measures, speech-in-noise laboratory testing, and real-world measures via questionnaires and ecological momentary assessment.Study sample Seventeen normal-hearing individuals (9 female, 21–59 years) with self-reported hearing-in-noise difficulties.Results Acoustic measures showed that, relative to unaided, AirPods Pro provided a SNR advantage of +5.4 dB. Speech intelligibility performance in laboratory testing increased 11.8% with AirPods Pro, relative to unaided. On average, participants trialling AirPods Pro in real-world noisy venues reported that their overall hearing experience was a bit better than without them. Five participants (29%) reported that they would continue using AirPods Pro in the future. The most relevant barriers that would discourage their future use were limited hearing benefit, discomfort, and stigma.Conclusions Occasional use of AirPods Pro may help some individuals with normal audiograms ameliorate their speech-in-noise hearing difficulties. The identified barriers may inspire the development of new technological solutions aimed at providing an optimal management strategy for the hearing difficulties of this segment of the population.
近年, 家庭で実施可能な簡便な聴力評価としてヒアラブルデバイスの応用が進みつつあるものの, その測定精度は十分に検証されていない. 本研究では, AirPods Pro2 に搭載されたヒアリングチェックの臨床的有用性を検討するため, ボランティア140例を対象に純音聴力検査との比較を行った. 周波数別の比較では 250, 500, 1k, 2k, 6kHz に統計学的な有意差を認め, 特に低音域では閾値差が ±5dB 以内に収まった割合が 250Hz で14％, 500Hz で36％と低値であり, デバイス閾値が低く測定される臨床的にも無視できない乖離が見られた. 20dB 以上の左右差を有する感音難聴50例における解析では, 1kHz で左右差 75dB 以上, 2kHz で 65dB 以上の症例に異常低値が出現し, 交叉聴取の関与が示唆されたが, 本デバイスの測定上限である気導閾値 85dB 以下に限定すると左右差による偏りは認めなかった. 操作性においては多くの被検者が容易と感じたが, 高齢者では操作困難例が多く, 32例で補助を要した. 以上より, ヒアリングチェックは簡便な測定手段として一定の有用性を有する一方, 低音域の誤差や交叉聴取の影響については注意が必要である. 自覚症状のある場合には従来の聴力検査が不可欠であり, 本デバイスは補助的手段としての利用が適切と考えられた.
The data were collected to evaluate usability, functionality, comfort, risk perception, social influence, habituation, intention for continuous use, and satisfaction related to AirPods-based physical activity measurement.The dataset includes item-level Likert-scale responses (1 = strongly disagree to 5 = strongly agree), organised in a wide format where each row represents a participant and each column represents a survey item. No personally identifiable information is included. This dataset is shared in accordance with the journal’s open data policy to enable independent verification and reproducibility of the reported quantitative usability evaluation results.
Apple's AirPods have helped forge a multibillion-dollar market for true wireless hearable devices. The article employs media geology and political ecology to argue that AirPods exemplify the Capitalocene, a time where a planetary sociotechnical system based on ecologically unequal exchange benefits a privileged minority of humans while inflicting significant harms to humans and ecosystems that will persist across inhuman temporalities. These harms are inequitably distributed and are not typically experienced by those who can afford luxury items such as AirPods. While digital technologies are often mistaken for dematerialised objects that will enable infinite economic growth on a materially finite planet, examining the flows of energy, matter, labour and knowledge required for the production and maintenance of these devices comprehensively refutes these claims. AirPods are designed to function for just eighteen to thirty-six months of daily use before planned obsolescence renders AirPods as long-lived, toxic, electronic waste. Pending ‘right to repair’ legislation should prohibit the production of irreparable digital devices such as AirPods, as the right to repair an irreparable device is effectively meaningless.
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Individuals with hearing loss face high personal costs for hearing aids, making over-the-counter hearing aids, or headphones featuring custom audio control, alternative options. Apple Inc.’s AirPods Pro contain a variety of headphone accommodations designed to help people hear better. Recent research has evaluated electroacoustic properties of AirPods Pro demonstrating good ability to fit prescriptive targets for different hearing losses. Here, we examine the usability and perceived benefit from engaging custom audiogram-driven features in AirPods Pro. Listeners with self-assigned hearing profiles ranging from normal hearing to a moderate degree of hearing loss take a hearing screener (Mimi Hearing Test). AzBio sentences are presented in quiet and in noise while enabling and disabling the custom features in Apple’s Accommodation settings. Listeners then complete a questionnaire targeting usability of the features and perceived benefit. Overall, listeners found the features easy to use and to be at least a little helpful, regardless of hearing status. Listeners with suspected or known hearing impairment reported slightly greater benefit than normal hearing listeners when listening in noise with the custom audio features. Headphone Accommodations in AirPods Pro may be an affordable option for an improved listening experience. [Work supported by Western Washington University.]
Imitation learning requires high-quality demonstrations consisting of sequences of state-action pairs. For contact-rich dexterous manipulation tasks that require dexterity, the actions in these state-action pairs must produce the right forces. Current widely-used methods for collecting dexterous manipulation demonstrations are difficult to use for demonstrating contact-rich tasks due to unintuitive human-to-robot motion retargeting and the lack of direct haptic feedback. Motivated by these concerns, we propose DexForce. DexForce leverages contact forces, measured during kinesthetic demonstrations, to compute force-informed actions for policy learning. We collect demonstrations for six tasks and show that policies trained on our force-informed actions achieve an average success rate of 76% across all tasks. In contrast, policies trained directly on actions that do not account for contact forces have near-zero success rates. We also conduct a study ablating the inclusion of force data in policy observations. We find that while using force data never hurts policy performance, it helps most for tasks that require advanced levels of precision and coordination, like opening an AirPods ca
By 2050, a quarter of the US population will be over the age of 65 with greater than a 40% risk of developing life-altering neuromusculoskeletal pathologies. The potential of wearables, such as Apple AirPods and hearing aids, to provide personalized preventative and predictive health monitoring outside of the clinic is nascent, but large quantities of open-ended data that capture movement in the physical world now exist. Algorithms that leverage existing wearable technology to detect subtle changes to walking mechanics, an early indicator of neuromusculoskeletal pathology, have successfully been developed to determine population-level statistics, but individual-level variability is more difficult to parse from population-level data. Like genetic sequencing, the individual's gait pattern can be discerned by decomposing the movement signal into its fundamental features from which we can detect "mutations" or changes to the pattern that are early indicators of pathology - movement-based biomarkers. We have developed a novel approach to quantify "normal baseline movement" at an individual level, combining methods from gait laboratories with methods used to characterize stellar oscillat
Recent advancements in teleoperation systems have enabled high-quality data collection for robotic manipulators, showing impressive results in learning manipulation at scale. This progress suggests that extending these capabilities to robotic hands could unlock an even broader range of manipulation skills, especially if we could achieve the same level of dexterity that human hands exhibit. However, teleoperating robotic hands is far from a solved problem, as it presents a significant challenge due to the high degrees of freedom of robotic hands and the complex dynamics occurring during contact-rich settings. In this work, we present ExoStart, a general and scalable learning framework that leverages human dexterity to improve robotic hand control. In particular, we obtain high-quality data by collecting direct demonstrations without a robot in the loop using a sensorized low-cost wearable exoskeleton, capturing the rich behaviors that humans can demonstrate with their own hands. We also propose a simulation-based dynamics filter that generates dynamically feasible trajectories from the collected demonstrations and use the generated trajectories to bootstrap an auto-curriculum reinfo
Modeling complementary relationships greatly helps recommender systems to accurately and promptly recommend the subsequent items when one item is purchased. Unlike traditional similar relationships, items with complementary relationships may be purchased successively (such as iPhone and Airpods Pro), and they not only share relevance but also exhibit dissimilarity. Since the two attributes are opposites, modeling complementary relationships is challenging. Previous attempts to exploit these relationships have either ignored or oversimplified the dissimilarity attribute, resulting in ineffective modeling and an inability to balance the two attributes. Since Graph Neural Networks (GNNs) can capture the relevance and dissimilarity between nodes in the spectral domain, we can leverage spectral-based GNNs to effectively understand and model complementary relationships. In this study, we present a novel approach called Spectral-based Complementary Graph Neural Networks (SComGNN) that utilizes the spectral properties of complementary item graphs. We make the first observation that complementary relationships consist of low-frequency and mid-frequency components, corresponding to the relev
The proliferation of wearable technology has established multi-device ecosystems comprising smartphones, smartwatches, and headphones as critical enablers for ubiquitous pedestrian localization. However, traditional pedestrian dead reckoning (PDR) struggles with diverse motion modes, while data-driven methods, despite improving accuracy, often lack robustness due to their reliance on a single-device setup. Therefore, a promising solution is to fully leverage existing wearable devices to form a flexiwear bodynet for robust and accurate pedestrian localization. This paper presents Suite-IN++, a deep learning framework for flexiwear bodynet-based pedestrian localization. Suite-IN++ integrates motion data from wearable devices on different body parts, using contrastive learning to separate global and local motion features. It fuses global features based on the data reliability of each device to capture overall motion trends and employs an attention mechanism to uncover cross-device correlations in local features, extracting motion details helpful for accurate localization. To evaluate our method, we construct a real-life flexiwear bodynet dataset, incorporating Apple Suite (iPhone, App
For more than a century, pianists and music teachers have argued over whether a performer’s touch can actually change the tone color of a piano note — and now scientists say the answer is yes。 Using a cutting-edge sensor system that tracked piano key movements at 1,000 frames per second, researchers discovered that elite pianists subtly manipulate