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Smartek AI Bed: Where Sleep Science Meets Smart Living

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NEW ZEALAND BED COMPANY |

Authored by Dr. Moses Satralkar, Sleep Specialist

Paradigm shifts in evolutionary technology continue to make progress across the world to enhance lifestyle (Djanian et al., 2022; Lujan et al., 2021). The evolution of AI sleep systems showed basic functionality within homes in New Zealand. Practical industrial market research revealed sleep technology being used mostly within medical facilities, but comparative analysis indicated the lack of a fully integrated, affordable AI-sleep ecosystem within homes. The Smartek AI bed was created by Serene Life and New Zealand Bed Company to fill this technological gap after two years of intense research, internal deliberations and international collaboration with MPE Italia. Sleep is a rather important physiological activity. An average human spends a third of his or her life sleeping in bed. Research has shown that different types of mattresses affect spinal alignment, joint pressure and sleep architecture differently based on individual’s body mass index, height, weight, sleeping positions, etc. The Smartek AI bed is a unique dynamic surface powered by electrical control systems, that can be used by all and can adapt to the needs of all uniformly. It is thus uniquely distinct from a static-analog sleep surface that has fixed physical structure comprised of foam/gel/latex and springs with fixed firmness that does not actively adjust real-time; therefore, suitable for one individual or at most a couple.

Smart beds combine AI-driven analytics using sensors and adaptive biomechanics using a mattress that is therapeutic as a sleep surface since it provides real-time postural alignment throughout the night (Djanian et al., 2022). They have the potential to influence an individual’s sleep and health differently, particularly adults. Smart beds use multiple biosensors for pressure sensing and modulate the dynamic sleep surface for pressure redistribution of each user, irrespective of his or her height or weight or age (Lin et al., 2023). The Smartek AI seems to be a remarkable breakthrough as a smart-bed in New Zealand as its advanced features include effects of adaptive firmness, cradling, zero-G, relaxation, decompression, etc. which have the potential to restore and rehabilitate the body. The Smartek AI could shape behavior and customize lifestyles owing to comprehensive health metrics generated via its Sleep App. The Smartek AI bed’s main strengths are customization, monitoring, and automation. Its biggest health related claim is that it may help users achieve a more comfortable, better-monitored sleep environment with tailored support and pressure redistribution.

Circadian neuroscience offers promise to enhance brain function, recuperation, and long-term health by matching endogenous biological cycles with external environmental cues. This can be uniquely facilitated by tailoring sleep surfaces that directly influence deeper sleep or sleep latency, sleep efficiency and sleep cycle duration (Satralkar, 2026). It is important for us to reorient our focus to understand that static sleep surfaces and dynamic sleep surfaces are both very different. Culturally and traditionally more use of static surfaces/beds/mattresses is currently afforded in society. However, on newer provisions, the options of choice, availability, perception and comparison of innovations can be systematically carried out qualitatively and quantitatively. Dynamic surfaces may influence user satisfaction differently when compared with static surfaces (Satralkar et. all, 2026 unpublished); and a comparative analysis of ethnic preferences in user satisfaction for sleep surfaces may also be seen (Satralkar et. al, 2026 unpublished). AI enabled sleep systems may allow for better entrainment of circadian sleep-wake rhythm via the body clock with external sleep surfaces since they can be modulated by zeitgebers (time givers) like, temperature, light, relative humidity, pressure redistribution, other cues, etc. (Lujan et al., 2021; Satralkar, Unpublished). Therapeutic surfaces have been recommended for restorative support for alleviation of sleep disorders as well as rehabilitation both of which are essential in New Zealand (Satralkar, 2026). Eventually, usage of such automated sleep eco-systems could perhaps increase social awareness to improving sleeping habits and diets to mitigate risks of different health issues.

To break down its composition, internally the smart bed is composed of an integrated, multi-layered system combining embedded physiological sensing, adaptive support structures. At its core, sophisticated architecture of the sleep system surface comprises high grade breathable materials with multiple zones, gel-foam layer, and a foot constant-temperature graphene heating feature with air chambers. Adaptive air-cells as a responsive sleep system adjust firmness and load distribution across multiple zones to maintain best possible ergonomic support (Lin et al., 2023). The physiological sensing through the mattress architecture allows minute pressure detection and sleep monitoring (Askjer et al., 2023). It has a broad ecosystem integration, because it can connect with diverse sleep bands and pressure detection modules. The highly granular body-zone support has six zones for hardness adjustment which is useful for customizing spinal alignment and comfort to different body types. The mattress has a nice thick girth with excellent comfort fit. The smart mattress can be elevated upwards and downwards based on voice or remote. The mattress has a preset memory and auto-inflation, so different users can return it to preferred settings quickly and maintain consistency. A central AI control unit in smart-bed processes real-time data using machine learning algorithms to infer sleep patterns and health related data (Djanian et al., 2022).

It is essential to clear the misunderstanding that AI-Sleep Systems are only suitable for hospitals, clinics or elderly patients although this is true as a fact (Ziegler, 2024; Hong 2018), they can be used by normal healthy people. Infact, most individuals may favor a dynamic surface after knowing its advantages or after trialing its amazing snug comfort-fit, it is just the cost factor that may be limiting initially, but that may change in future. The Smartek AI being neuro-adaptive has potential to enhance sleep health in normal healthy adults but those with sleep disorders or orthopedic issues will probably benefit more as earlier studies showed smart-beds highly relevant for orthopaedic rehabilitation, musculoskeletal recovery, chronic back pain, and posture optimization (Verhaert et al., 2013). Monitoring of sleep and physiological signals may help users identify, track, and regulate sleep disruption patterns and independently adjust sleep habits (Lujan et al., 2021). The prescriptive and predictive recommendations of Smartek AI may be investigated via its robust Sleep App (Satralkar, Unpublished). The Smart Bed is uniquely manufactured in different sizes along-with electronics and a user manual crafted as a Long Single, Queen or Super King (two independent long singles with independent remotes). To conclude static or conventional sleep-systems (mattresses and beds) are equally well positioned to provide excellent sleep and health, it is just evolutionary technology which being all pervasive allowed  for more improvisation to create another remarkable and distinct sleep eco-system to benefit a wide array of human physiologic functions.

Photograph of “Smartek AI” launched by Serene Life and New Zealand Company

Photograph of “Smartek AI” launched by Serene Life  at New Zealand Bed Company, Pukekohe

Scientific Research on Key Features and Associated Health Benefits of AI-Sleep Systems

  1. AI Sleep Physiology Monitoring: Embedded with ballistocardiography (BCG) and pressure sensors advanced AI-sleep systems can enable continuous real-time monitoring of heart rate, respiration, and micro-movements, supporting detection of sleep disturbances. (Yu, et al., 2023; Lin et al., 2023; Askjer et al., 2023).
  2. Multi-Zone Adaptive Pressure Control: A smart bed system capable of adaptive body-pressure control was shown to optimize spinal alignment, pressure dispersion, and biomechanical support across different body shapes and sleeping positions, relevant to orthopaedic recovery and rehabilitation (Yu et al., 2023; Ito & Usuki, 2024).
  3. Automated Postural Adjustment: AI-driven systems enable real-time adaptation of sleep surfaces in response to body movement and posture changes, with the aim of reducing prolonged static loading and improving sleep comfort. Smart bedding systems using sensors and intelligent monitoring improved perceived sleep comfort and contribute to better sleep quality (Bai et al; 2024; Djanian et al., 2022).
  4. Circadian Rhythm Support System: Sleep timing and environmental synchronisation are influenced by circadian regulation and multisensory sleep monitoring systems, which may contribute to improved physiological sleep outcomes. AI-driven smart sleep systems can adapt to user sleep patterns to enhance sleep quality and overall well-being (Gamel., 2024).
  5. Real-Time Health Analytics: Continuous long-term data processing enables AI-generated sleep insights and feedback based on physiological signals, supporting personalised sleep monitoring and trend analysis. (Djanian et al., 2022).
  6. Therapeutic Pressure-Relief Surface: An AI-enabled smart mattress has shown to improve pressure redistribution, reduced prolonged tissue loading, and supported healing in patients with chronic pressure injuries, highlighting applications in orthopaedic rehabilitation, elderly care, and long-term musculoskeletal recovery (Ni et al., 2024)
  7. Neurotechnology–Biomechanics Integration: This combines sleep neuroscience and physiological sensing to support integrated sleep monitoring and data-driven sleep-health systems rather than passive resting surfaces. (Lujan et al., 2021). Research also showed that smart beds equipped with force sensors can continuously monitor nocturnal cardiovascular and autonomic nervous system activity, potentially assisting early detection of neurological and cardiovascular dysfunction (Winger & Garcia-Molina, 2024)
  8. Non-Invasive Continuous Monitoring: Passive sensing without wearables or user intervention enables unobtrusive sleep monitoring and supports continuous physiological data collection during natural sleep conditions without interrupting sleep-cycles (Askjer et al., 2023).
  9. Personalized Adaptive Sleep Environment: AI-driven systems enable personalised analysis of physiological sleep data and provide real-time feedback tailored to individual sleep behaviour to support sleep optimisation. (Djanian et al., 2022). They can also reduce immobility (Ziegler et al., 2024)
  10. Preventive Health Positioning: Studies have demonstrated that AI and machine-learning algorithms embedded within smart bed systems can identify insomnia risk and support early neurological and sleep-health intervention. (Winger et al., 2024). Smart bed studies also demonstrated associations between sleep timing, sleep regularity, cardiorespiratory health, and sleep metrics collected through smart bed technology (Garcia Molina et al., 2024).

References

  • Askjer, S., Mathiasen, K., Amidi, A., Parsons, C., & Ladegaard, N. (2023). Real-time unobtrusive sleep monitoring of in-patients with affective disorders: A feasibility study. Electrical Engineering and Systems Science. https://arxiv.org/abs/2311.13457
  • Bai, X., Liu, Y., Dai, Z., Chen, Y., Fang, P., & Ma, J. (2024). Determinants of perceived comfort: Multi-dimensional thinking in smart bedding design. Sensors, 24(13), 4058. https://doi.org/10.3390/s24134058
  • Djanian, S., Bruun, A., & Nielsen, T. D. (2022). Sleep classification using consumer sleep technologies and AI: A review of the current landscape. Sleep Medicine, 100, 390–403. https://doi.org/10.1016/j.sleep.2022.09.004
  • Garcia-Molina, G., Chellamuthu, V., Lea, B., Aloia, M., Wu, M., & Mills, R. (2024). Observational study to understand the effect of timing and regularity on sleep metrics and cardiorespiratory parameters using data from a smart bed. Chronobiology International, 41(2), 213–225. https://doi.org/10.1080/07420528.2023.2298267
  • Gamel, S. A., & Talaat, F. M. (2024). SleepSmart: An IoT-enabled continual learning algorithm for intelligent sleep enhancement. Neural Computing and Applications, 36, 4293–4309. https://doi.org/10.1007/s00521-023-09310-5
  • Gao, C., Haghayegh, S., Wagner, M., Cai, R., Hu, K., Gao, L., & Li, P. (2023). Approaches for assessing circadian rest-activity patterns using actigraphy in cohort and population-based studies. Current Sleep Medicine Reports, 9(4), 247–256. https://doi.org/10.1007/s40675-023-00267-4
  • Hong, Y.-S. (2018). Smart care beds for elderly patients with impaired mobility. Wireless Communications and Mobile Computing, 2018, 1780904. https://doi.org/10.1155/2018/1780904
  • Ito, J., & Usuki, S. (2024). Measurement and control of body pressure towards smart bed system. International Journal of Automation Technology, 18(1), 104–111. https://doi.org/10.20965/ijat.2024.p0104
  • Lin, C.-L., Sun, Z.-T., & Chen, Y.-Y. (2023). Air-mattress system for ballistocardiogram-based heart rate and breathing rate estimation. Heliyon, 9(1), e12717. https://doi.org/10.1016/j.heliyon.2022.e12717
  • Lujan, M. R., Perez-Pozuelo, I., & Grandner, M. A. (2021). Past, present, and future of multisensory wearable technology to monitor sleep and circadian rhythms. Frontiers in Digital Health, 3, 721919. https://doi.org/10.3389/fdgth.2021.721919
  • Ni, T. F., Wang, J.-L., Chen, C.-K., Shih, D. F., & Wang, J. (2024). Can a prolonged healing pressure injury be benefited by using an AI mattress? A case study. BMC Geriatrics, 24, Article 307. https://doi.org/10.1186/s12877-024-04900-x
  • Satralkar, M. (2026). Alleviating sleep disorders through targeted intervention using restorative-therapeutic surfaces in New Zealand. Journal of Clinical Neuroscience and Rehabilitation, 2(1), 27–31. https://www.neuroscigroup.com/jcnr/article/view/JCNR-2-105
  • Satralkar, M. (2026). Advancing AI-sleep systems to restore neurological sleep disorders and precision health in New Zealand. Journal of Neuroscience and Neurological Disorders, 10(1), 15–24. https://doi.org/10.29328/journal.jnnd.1001116
  • Verhaert, V., Van Deun, D., Verbraecken, J., Vandekerckhove, M., Exadaktylos, V., Haex, B., & Vander Sloten, J. (2013). Smart control of spinal alignment through active adjustment of mechanical bed properties during sleep. Applied Intelligence, 5(4), 547–562. https://doi.org/10.3233/AIS-130216
  • Winger, T., Chellamuthu, V., Guzenko, D., Aloia, M., Barr, S., DeFranco, S., Gorski, B., Mushtaq, F., & Garcia-Molina, G. (2024). Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform. Frontiers in Neurology, 15, 1303978. https://doi.org/10.3389/fneur.2024.1303978
  • Winger, T., & Garcia-Molina, G. (2024). Unobtrusive detection of heart-rate-dipping during sleep based on force sensors in a smart bed. Sleep, 47(Supplement_1), A140. https://doi.org/10.1093/sleep/zsae067.0326
  • Yu, W., Zhu, Y., Zhou, Y., Yu, X., Lin, H., Ruan, L., Lei, H., & Luo, Y. (2023). Investigating the influence of an adjustable zoned air mattress on sleep: A multinight polysomnography study. Frontiers in Neuroscience, 17, 1160805. https://doi.org/10.3389/fnins.2023.1160805
  • Ziegler, S., Schmoor, C., Schöler, L. M., Schepputat, S., Takem, E., Grotejohann, B., Steinbrenner, I., & Feuchtinger, J. (2024). Potential for reducing immobility times of a mobility monitor in-bed sensor system: A stepped-wedge cluster-randomised trial. BMC Nursing, 23, 32. https://doi.org/10.1186/s12912-023-01658-2
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