Custom App Research Articles

Automated, Real-Time Integration of Biometric Data From Wearable Devices With Electronic Medical Records: A Feasibility Study.

A major barrier to the incorporation of biometric data into clinical practice is the lack of device integration with electronic medical records (EMRs). We developed infrastructure to transmit biometric data from an Apple Watch into the EMR for physician review. The study objective was to test feasibility of using this infrastructure for patients undergoing radiotherapy. The study included patients with breast or prostate cancer receiving ≥3 weeks of radiotherapy who reported owning an Apple Watch. Daily resting heart rate (HR), HR variability, step count, and exercise minutes were automatically transferred to our EMR using a custom app installed on each patient's iPhone. Biometric data were presented to the treating radiation oncologist for review on a weekly basis during creation of the on-treatment note. Feasibility was defined a priori as physician review of biometric data for at least 90% of patients. Time trends in biometric data were tested using the Jonckheere-Terpstra test. Patient satisfaction was assessed using the System Usability Scale (SUS), with scores above 80 considered above-average user experience. Of the 20 patients enrolled, biometric data were successfully transmitted to the EMR and reviewed by the radiation oncologist for 95% (n = 19) of patients, thus meeting the a priori feasibility threshold. For patients with radiation courses ≥4 weeks, exercise minutes decreased over time (P = .01) and daily mean HR variability increased over time (P = .02). The median SUS was 82.5 (IQR, 70-87.5). Our study demonstrates the feasibility of real-time integration of biometric data collected from an Apple Watch into the EMR with subsequent physician review. The high rates of physician review and patient satisfaction provide support for further development of large-scale collection of wearable device data.

Janus Separation-Sensing Membrane Hosted with Enzyme@MOF Nanoreactor for Real-Time Blood Sensing.

Plasma separation, rich in biomarkers crucial for diagnosis, is conventionally achieved via high-speed centrifugation, a method hindered by its blood usage, lengthy processes, and complex operations, which delays detection. We introduced a novel real-time blood sensing method based on a Janus membrane and enzymes @MOFs. Asymmetric driving of the janus membrane can realize spontaneous separation of plasma and prevent hemolysis during direct separation. Glucose oxidase (GOx), uric acid oxidase (UOx) and horseradish peroxidase (HRP) were encapsulated in a hydrophilic organometallic framework (MOFs) to construct an enzyme cascade nanoreactor. Embedding enzyme in hydrophilic MOFs not only retains the natural conformation of free enzyme but also improves the brittleness of enzyme, endows MOFs with new biological functions, and expands its sensing application. Using 3,3',5,5'-tetramethylbenzidine (TMB) as a chromogen and a custom app for color interpretation, we achieved real-time visualization of glucose (Glu) and uric acid (UA) at a 50 μM limit. The system accurately analyzed serum samples, matching commercial kits and showing promise for portable, personalized diagnostics.

Help Them Understand: Testing and Improving Voice User Interfaces

Voice-based virtual assistants are becoming increasingly popular. Such systems provide frameworks to developers for building custom apps. End-users can interact with such apps through a Voice User Interface (VUI), which allows the user to use natural language commands to perform actions. Testing such apps is not trivial: The same command can be expressed in different semantically equivalent ways. In this article, we introduce VUI-UPSET, an approach that adapts chatbot-testing approaches to VUI-testing. We conducted an empirical study to understand how VUI-UPSET compares to two state-of-the-art approaches (i.e., a chatbot testing technique and ChatGPT) in terms of (i) correctness of the generated paraphrases, and (ii) capability of revealing bugs. To this aim, we analyzed 14,898 generated paraphrases for 40 Alexa Skills. Our results show that VUI-UPSET generates more bug-revealing paraphrases than the two baselines with, however, ChatGPT being the approach generating the highest percentage of correct paraphrases. We also tried to use the generated paraphrases to improve the skills. We tried to include in the voice interaction models of the skills (i) only the bug-revealing paraphrases, (ii) all the valid paraphrases. We observed that including only bug-revealing paraphrases is sometimes not sufficient to make all the tests pass.

Rapid and portable quantification of HIV RNA via a smartphone-enabled digital CRISPR device and deep learning

For the 29.8 million people in the world living with HIV/AIDS and receiving antiretroviral therapy, it is crucial to monitor their HIV viral loads. To this end, rapid and portable diagnostic tools that can quantify HIV RNA are critically needed. We report herein a rapid and quantitative digital CRISPR-assisted HIV RNA detection assay that has been implemented within a portable smartphone-based device as a potential solution. Specifically, we first developed a fluorescence-based reverse transcription recombinase polymerase amplification (RT-RPA)-CRISPR assay that can efficiently detect HIV RNA at 42 °C. We then implemented this assay within a commercial stamp-sized digital chip, where RNA molecules were quantified as strongly fluorescent digital reaction wells. The isothermal reaction condition and the strong fluorescence in the digital chip simplified the design of thermal and optical modules, allowing us to engineer a palm-size device measuring 70 × 115 × 80 mm and weighing less than 0.6 kg. We also capitalized the smartphone by developing a custom app to control the device, perform the digital assay, and capture fluorescence images throughout the assay using the smartphone's camera. Moreover, we trained and verified a deep learning-based algorithm for analyzing fluorescence images and identifying positive digital reaction wells with high accuracy. Using our smartphone-enabled digital CRISPR device, we successfully detected as low as 75 copies of HIV RNA in just 15 min, showing its potential toward monitoring of HIV viral loads and aiding the global effort to combat the HIV/AIDS epidemic.

From engagement to retention: Unveiling factors driving user engagement and continued usage of mobile trading apps

The popularity of online mobile trading has led to an increase in the development of mobile stock trading applications. Despite this increase in popularity, there is a dearth of empirical studies that examine the factors influencing the continued usage intention of these applications (hereafter, apps). Drawing on stimulus-organism-response (S-O-R) theory, this paper investigates the features of stock trading apps that generate consumer engagement and consequently, continued app usage intention. In study 1, through semi-structured interviews, we establish four key drivers of customer engagement with stock trading apps, and two possible moderators influencing the relationship between customer app engagement and continued usage intention. In study 2, we examine these key drivers by surveying stock trading app users from three different Facebook stock trading communities. The results confirm that the social presence and security features of these apps are significantly associated with consumer stock trading app engagement. We also find that fear of uncertainty and perceived corporate hypocrisy weaken the effect of customer app engagement on continued app usage intention. The study findings add to the literature on app usage and customer engagement and provide insights for fintech service companies to help them understand the factors that enhance consumer engagement with these apps.

The Height Pole: Measuring plot height using a single‐point LiDAR sensor

AbstractPlant canopy height is an essential trait for phenomics and plant breeding. Despite its importance, height is still largely measured by manual means with a ruler and notepad. Here, we present the Height Pole, a novel single‐point LiDAR (SPL)‐based instrument to measure and record plant and canopy height in the field quickly, reliably, and accurately. An SPL was mounted on the top of a pole and aimed downwards at an adjustable paddle that was positioned at the desired height. A custom app, written for Android OS, saved the plant height data from the SPL to a tablet. The Height Pole was tested against a ruler in the lab, in a field trial setting, and by multiple operators. Indoor and outdoor testing found no significant differences between a ruler and the Height Pole measurements. A test with five operators revealed that measuring, recording, transcribing, and digitizing were on average 20 s per plot slower with a ruler than with the Height Pole. The Height Pole required only one operator to measure and record data, reduced operator fatigue, and by directly writing the data to a .CSV file eliminated transcription errors. These improvements make it easier to collect crop height data on large experiments rapidly and accurately with low input costs.

Abstract 15144: The Heartline Experience: Can Digital Health Technology Trials Achieve Long-Term Participant Engagement?

Background: Digital health technologies can potentially streamline clinical research and reach broad and diverse populations, yet they often fall short to achieve long-term participant engagement. Research Question: Can long-term participant engagement in a large, direct-to-participant randomized trial using an entirely decentralized trial platform be achieved? Methods: Heartline (www.heartline.com) was designed to test the impact of a mobile app-based heart health program with the electrocardiogram (ECG) and Irregular Rhythm Notification (IRN) features on Apple Watch for early diagnosis, treatment, and outcomes of atrial fibrillation (AF). The custom app collected electronic patient-reported outcomes (ePROs) and incorporated engagement strategies based on behavioral science principles and features relevant to the target study population, including a simple interface and actions, weekly curated health education, and points-based incentives. Participants who received an IRN from their Apple Watch were prompted to take an ECG and follow up with their healthcare provider (HCP). Results: From 02/2020 to 12/2022, Heartline enrolled 34,245 US participants ≥65 years of age. To date, 80% and 76% of the study population used the app at least monthly after 1 and 2 years, with weekly engagement rates of 68% and 66%, respectively ( Figure ). ePRO response rates were 88% at 1 year and 91% at 2 years. Of those receiving an IRN, 85% took an ECG recording and 71% reported follow up with their HCP. Conclusions: The Heartline study achieved high rates of sustained, longitudinal engagement, with >75% of the study population remaining engaged over 2 years and >90% completing ePROs, representing substantially higher engagement rates than prior remote digital health studies. A combination of strategies was used to promote engagement in Heartline. Further work to identify the key drivers of engagement will inform future decentralized trials and digital therapeutics.

Web Framework

The purpose of this paper is to demonstrate how to extend a web framework (WF) for developing web apps that can meet the diverse needs of different fields. This work aims to provide computer science senior project students with an opportunity to work on a wide range of projects using Apache, HTML, CSS, JavaScript, PHP, and MySQL. The paper presents a WF that manages jokes, based on the book PHP MySQL: Novice to Ninja (6th ed.) by T. Butler and K. Yank. The WF is interactive, responsive, secure, password-protected and database-driven. It consists of generic and specific components that use a MySQL database on the web server-side to store jokes. The specific components can be modified or added to create custom apps. Admin users can log in with their email and password and perform various actions (view, add, delete and edit) on jokes and categories, depending on their level of permission. Admins with full permissions can also edit the permissions of other registered accounts and access the Manage Categories, Manage Jokes, and Manage Authors pages. The original WF is enhanced and expanded for the senior project starting point by adding the following features: a hierarchical main menu with admin menus as the second level, a highlighted selected menu item, responsive design - accessibility on all devices, improved password security (minimum of 8 characters, 1 lowercase letter, 1 uppercase letter, 1 digit, 1 special character), a two-factor authentication security subsystem, cascaded deletion and uploaded and retrieved binary files (images and documents). In conclusion, the project is designed and implemented following the best practices and tested on a web hosting server provider.

A comprehensive analysis of the coverage and performance of 4G and 5G deployments

5G New Radio (5G NR), has now been deployed in all available bands: low (<1 GHz), mid (1–6 GHz), and high (>24 GHz), each with a trade-off between coverage and throughput/latency performance. The preceding 4G Long Term Evolution (4G LTE) networks also catching up to 5G mid-band by deploying in the unlicensed 5 GHz (using License Assisted Access/LAA) and the 3.5 GHz Citizens Broadband Radio Service (CBRS). We present a comprehensive analysis of 4G and 5G deployments in Chicago and Miami, focusing on coverage, throughput, and latency performance in low-, mid-, and high-bands, under several scenarios: outdoor, outdoor-to-indoor, and under high temperature. To measure deployed networks, we utilized a scalable methodology with commercial and custom apps to collect detailed signal data (e.g., signal strength, cell ID, throughput) on user device (i.e., smartphones). The analysis based on our measurements yields the following findings: (i) when comparing the throughput and latency performance of optimized mid-band 4G networks to 5G (both standalone (SA) and non-standalone (NSA)), they exhibit comparable performance, and (ii) it is important to note that mmWave 5G has the capability to deliver multi-Gbps throughput, even in NSA mode. However, this high-speed performance is susceptible to limitations imposed by factors such as distance, body interference, obstructions (such as Low-e glass), and overheating, which can render its performance less reliable. Therefore, even though 5G exhibits considerable potential in its early stages, additional efforts are required to guarantee that the stated goals of 5G are met.

Master Data Management Disruptive Modern Architecture

Master Data Management concept came into the mainstream during the mid 2000s, to generate a single Golden profile of the customer. As the Enterprise IT architecture started adopting and integrating various Commercial Products for CRM, ERP, Finance, HR, Supply Change Management etc. along with other homegrown custom apps, the information around the Customer started drifting across the various applications which resulted in various inefficiencies in day-to-day business operation. Master Data Management tools and technologies provide a way to perform identity resolution and survive latest and greatest information. Traditionally, the MDM tools always adopt a lean approach that uses minimal attributions to identify the smallest amount of (master) data with the biggest influence on business outcomes like Name, Phone, SSN, Email and Address. These constitute only less than 1% of the enterprise data, In the age of generative AI, there is a greater need to understand the context and the relationship of ALL data. This paper explores an alternate approach to mitigate the shortcomings of the currently available MDM tools

A device-independent method for the colorimetric quantification on microfluidic sensors using a color adaptation algorithm.

Ageneral and adaptable method is proposedto reliably extract quantitative information from smartphone images of microfluidic sensors. By analyzing and processing the color information of selected standard substances, the influence of light conditions, device differences, and human factors could be significantly reduced. Machine learning and multivariate fitting methods were proved to be effective for chroma correction, and a key element was the training ofsample size and the fitting form, respectively. A custom APP was developed and validated using a high-sensitivity chromium ion quantification paper chip. The average chroma deviations under different conditions were reduced by more than 75% in RGB color space, and the concentration test error was reduced by more than half compared with the commonly used method. The proposed approach could be a beneficial supplement to existing and potential colorimetry-based detection methods.

Compensatory articulatory behaviors after tongue reconstruction in production of English plosives: Establishing the range of lip movement patterns for control speakers

Head and neck cancer can have a devastating impact on speech and swallowing function. In particular, a tumor in the tongue can reduce the ability to produce articulatory gestures typical for English plosives. Previous case studies suggest that the lower lip can compensate for tongue tip gestures in speech after tongue reconstruction. The goal of this study is to establish typical lower lip movement patterns for alveolar and velar plosives for English speakers towards identifying compensatory lip movements in cancer speakers. Ten participants were recruited with no reported hearing or speech problems. A list of 40 minimal pairs beginning with the target plosives were created and embedded in carrier sentences. The participants read sentences in random order with and without babble noise in a standing posture. Lip motion was captured using a custom app on an iPhone 11 capturing the perioral surface area with the TrueDepth infrared camera. Preliminary results suggest that this app-based approach to lip tracking is a viable tool to capture typical lip movement patterns, ultimately enabling clinical research in more accessible settings. Mixed-effects linear regression analyses of the lip kinematics will be discussed.

Universal design space exploration for building energy design

This paper proposes novel improvements to existing Design Space Exploration (DSE) workflows that enable detailed performance-analysis at the speed of design for all projects at little cost. The authors refer to this methodology as Universal Design Space Exploration (UDSE). Rather than apply DSE to a project-specific challenge, UDSE enables a single pre-simulated design space to be applicable across many projects. The novel scalability of these “universal” design spaces justify investment in ML-powered apps that make pre-simulated analysis instantly accessible, affordable, and impactful across multiple projects. This research showcases the feasibility and potential benefit of UDSE by applying it to the challenge of early conceptual energy modeling. First, a group of experts crafts the input parameters and output metrics of a massive Design Space so that it encompasses the common problem. Then an automated parametric simulation workflow is developed to model and simulate any combination of input parameters. Several hundred thousand iterations are then simulated and analyzed. The result of this analysis guides the design of a prototype app which is powered by an AI surrogate model that allows users to receive instantaneous analysis about any design contained within the design space. This research shows that it is feasible to simulate the massive design spaces required by UDSE using currently available computational resources. We show that the surrogate modeling process is capable of accurately extending relatively limited simulation data to fully map the design space. We also show that these surrogate models can be effectively integrated into custom apps that can automate advanced DSE analysis and deliver insights to design teams in real-time. This paper concludes that UDSE offers a novel and scalable approach to early conceptual performance analysis.

Use of Laryngeal Sensors To Monitor Talk Time in People With Aphasia

<h3>Research Objectives</h3> To investigate the feasibility of using laryngeal sensors to monitor the amount of time people with aphasia talk in day-to-day life. <h3>Design</h3> Feasibility study. <h3>Setting</h3> Data was collected by the participants during an average day at home and in the community. <h3>Participants</h3> 10 participants with aphasia participated. Participants (mean [SD] age = 57.3 [5.5] years, 9 male) were diagnosed with aphasia following a left hemisphere stroke (Mean [SD] time post-onset = 57.2 [40.44] months). <h3>Interventions</h3> Participants were provided with all necessary equipment and given a training session by a speech language pathologist. Equipment included a small laryngeal sensor, adhesives, and an iPhone with a custom app to record the sensor data. Participants were trained to apply the sensor and connect to the app. Participants wore the sensor over the laryngeal notch of the throat for 8 hours a day for 1-3 days. Participants completed a structured interview about their experience with the sensors and activity during the data collection period. <h3>Main Outcome Measures</h3> Seconds of talk time per hour. <h3>Results</h3> Mean talk time per hour was 54.97 seconds (SD = 76.29 seconds). Sensor data was collected for a total of 22, 8 hour days. Participants were successful in setting up and using equipment in their home environment. Results of the structured interview reflect that the equipment was easy to use. <h3>Conclusions</h3> This study demonstrates the feasibility of measuring talk time in people with aphasia using a laryngeal sensor. The resulting data provides information about real world communication in this population and highlights their limited talk time. We discuss potential factors that may drive variability in talk time including aphasia characteristics (i.e., type, severity), demographic and environmental factors (gender, living arrangement, time spent at home). <h3>Author(s) Disclosures</h3> The authors have no conflicts to disclose.

Using a Mobile Phone to Demonstrate Thermal Properties of Materials

Mobile phones are a widely used platform for educational apps, mobile health, and a variety of chemical tests. Here, we are working on a mobile phone-based physics lab (mPhysics) that uses a mobile phone’s capabilities to run simple physics experiments and demonstrations. While a mobile phone can be used to analyze magnetic and optical properties of materials using built-in sensors, thermal analysis has never been incorporated into a mobile phone. Here, we propose to integrate thermochromic sensing with the image processing conducted on a mobile phone for in-class or in-lab demonstration of thermal properties of materials. We make inexpensive and nontoxic materials based on polydimethylsiloxane (PDMS) mixed with a thermochromic pigment that changes color from blue to white when heated. This material can be used to visualize such phenomena as change of temperature, thermal conductivity, and heat capacity. Then, the smartphone camera and a custom app can be used to track local color changes and translate them into different thermal properties. We propose to use this approach for experimental education of high school and undergraduate students.

AI 기술을 활용한 패션쇼핑물 앱 선호도 비교분석

With the development of Internet technology, online shopping has been activated, and e-commerce sites are emerging as the top shopping list. The purpose of the research is to identify the current status and characteristics of existing Chinese shopping app designs and present the direction of consumer use. The brands of Chinese clothing shopping apps collected from data and literature through research scope and method include pink bar apps, style matching apps, virtual dressing room apps, new salt apps, and fashion technology virtual fitting custom apps. It examines the comparative analysis of visual elements of the app, the factors that affect convenience for consumers, and the advantages of AI technology being applied to the app. Through the comparison of design elements by app, the influence of consumers' product purchases and the order of reflection of shopping apps became one of the important factors that spread technology and enhance cultural beyond simple online purchases, providing innovative progress in human society. Although shopping apps have grown the Chinese market, studies have shown that the visual elements of shopping apps are simple, so they have not formed their own brand image. Even in AI technology, AI intelligence still has technical difficulties because there is no all-round application system with simulation intelligence. Accordingly, Chinese fashion shopping apps are enriching shopping life by showing their own charm in fun and idea design, contributing to the brand image of companies and economic interests of companies, and increasing emotional experiences for consumers.

Deploying the Behavioral and Environmental Sensing and Intervention for Cancer Smart Health System to Support Patients and Family Caregivers in Managing Pain: Feasibility and Acceptability Study.

BackgroundDistressing cancer pain remains a serious symptom management issue for patients and family caregivers, particularly within home settings. Technology can support home-based cancer symptom management but must consider the experience of patients and family caregivers, as well as the broader environmental context.ObjectiveThis study aimed to test the feasibility and acceptability of a smart health sensing system—Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C)—that was designed to support the monitoring and management of cancer pain in the home setting.MethodsDyads of patients with cancer and their primary family caregivers were recruited from an outpatient palliative care clinic at an academic medical center. BESI-C was deployed in each dyad home for approximately 2 weeks. Data were collected via environmental sensors to assess the home context (eg, light and temperature); Bluetooth beacons to help localize dyad positions; and smart watches worn by both patients and caregivers, equipped with heart rate monitors, accelerometers, and a custom app to deliver ecological momentary assessments (EMAs). EMAs enabled dyads to record and characterize pain events from both their own and their partners’ perspectives. Sensor data streams were integrated to describe and explore the context of cancer pain events. Feasibility was assessed both technically and procedurally. Acceptability was assessed using postdeployment surveys and structured interviews with participants.ResultsOverall, 5 deployments (n=10 participants; 5 patient and family caregiver dyads) were completed, and 283 unique pain events were recorded. Using our “BESI-C Performance Scoring Instrument,” the overall technical feasibility score for deployments was 86.4 out of 100. Procedural feasibility challenges included the rurality of dyads, smart watch battery life and EMA reliability, and the length of time required for deployment installation. Postdeployment acceptability Likert surveys (1=strongly disagree; 5=strongly agree) found that dyads disagreed that BESI-C was a burden (1.7 out of 5) or compromised their privacy (1.9 out of 5) and agreed that the system collected helpful information to better manage cancer pain (4.6 out of 5). Participants also expressed an interest in seeing their own individual data (4.4 out of 5) and strongly agreed that it is important that data collected by BESI-C are shared with their respective partners (4.8 out of 5) and health care providers (4.8 out of 5). Qualitative feedback from participants suggested that BESI-C positively improved patient-caregiver communication regarding pain management. Importantly, we demonstrated proof of concept that seriously ill patients with cancer and their caregivers will mark pain events in real time using a smart watch.ConclusionsIt is feasible to deploy BESI-C, and dyads find the system acceptable. By leveraging human-centered design and the integration of heterogenous environmental, physiological, and behavioral data, the BESI-C system offers an innovative approach to monitor cancer pain, mitigate the escalation of pain and distress, and improve symptom management self-efficacy.International Registered Report Identifier (IRRID)RR2-10.2196/16178

Smartphone-based behavioral monitoring and patient-reported outcomes in adults with rheumatic and musculoskeletal disease

BackgroundRheumatic and musculoskeletal diseases (RMD) are associated with depression, fatigue, and disturbed sleep – symptoms that often impact behavior and activity. Patient reported outcomes (PROs) are a way of collecting information on the patient symptom experience directly from the individual. The purpose of this study was to measure and compare user smartphone sensor and activity data in adults with RMDs and assess associations with PROs.MethodsWe invited adults with RMDs enrolled in the FORWARD Databank to participate by installing a custom app on their smartphone and answering PROs (pain, global, HAQ-II) questions daily and weekly over 3 years. Passive data collected included mobility distance, unique calls and text messages, call durations, and number of missed calls. Confounders included sociodemographic, clinical, passive phone behavior, and seasonal factors. Kappa statistics between PRO and flares were computed to measure agreement. The agreement between daily and weekly VAS pain was estimated using the intraclass (ICC) correlation of a two-way random effect model. The relationship between the weekly PRO outcomes and the passive phone data was analyzed with a linear mixed-effect model (LMM), including a random intercept for participant and slope for time in the study with an unstructured covariate structure.ResultsOf the 446 participants, the mean (SD) age was 54 (12) years, most (65.5%) had rheumatoid arthritis (RA), the vast majority (91%) were female, and the US Northeast has the least representation (12%). Longer reaction times, interaction diversity, and higher mobility were associated with worse PROs while longer text messages were associated with better PROs. Participants in this study showed good levels of adherence which holds promise for future interventions using passive behavior measures in self-management and clinical follow-up.ConclusionThis is the first study to examine passive smartphone behavior with PROs in RMDs and we found significant associations between these behaviors and important health outcomes of pain and function. As smartphone usage continues to change, future studies should validate and expand on our findings with a goal of finding changes in patient symptoms passively through mobile device monitoring.

The Franka Emika Robot: A Reference Platform for Robotics Research and Education

The importance of robots for industry, research, education, and society as a whole is steadily increasing as reflected by the number of available systems and installed robots, not only in industry but also in the public sector and households. Software-only robotics researchers usually rely on commercially available robots which, in the case of manipulators, are primarily designed for industrial purposes and are often far from their needs. This article is a hands-on tutorial on the Franka Emika robot, the first series of industrial artificial intelligence (AI)-ready tactile robot platforms. Beyond industrial use, the systems can be seamlessly expanded to fulfill the demands of research and education across all robotics and AI disciplines. To satisfy the needs of such a wide variety of fields, it provides three different interfaces: Desk, a high-level app-based user interface for easy and fast task programming; Robot Integrated Development Environment (RIDE), a command-based programming environment used to create high-performance robot skills that enables programming custom apps and integrating external sensors; and the Franka control interface (FCI), a 1-kHz low-level torque and position control interface that exploits the also-available Langrangian dynamics robot model. We take a close look at implementations with all interfaces, ranging from simple solutions, apps, and controllers to robot-learning examples illustrating how to exploit all the advantages of this platform in ongoing robotics research and education.

When do we eat? Food ordering platform strategies

PurposeFood ordering apps have registered phenomenal growth during the pandemic and will continue to register high growth in the years to come. This paper has three objectives: first, to differentiate between the sales funnel of traditional vs platform businesses; second, to understand the sales funnel optimization strategies of a leading food ordering app in India; and third, to draw lessons from these strategies to enable food ordering apps in particular, and platform businesses in general to achieve the goals of customer acquisition and retention in a highly competitive market.Design/methodology/approachThe study is based on interviews conducted with company officials of two leading food delivery platforms in India, and a rich qualitative analysis of secondary data sources including news reports, government policies, reports and statistics from global consultancy firms. The interviews were analyzed to understand various stages of the sales funnel for the food ordering apps and the strategies implemented by the companies based on their understanding of the customer journey.FindingsThe findings of the study reveal that the sales funnel of a food ordering platform can be divided into three stages – top of the funnel (ToFu), middle of the funnel (MoFu) and bottom of the funnel. In the ToFu stage, the goal of the food ordering app is to create awareness about their brand and induce app download, which results in customer acquisition. In the MoFu stage, the food ordering app initiates personalized promotion strategies to trigger maximum orders via the app. The customer is then targeted for retention and referrals. Customer app navigation and order data are the most crucial inputs for navigating the sales funnel effectively.Research limitations/implicationsFirst, app-based service firms, especially food ordering platforms, can understand how the digital sales funnel can be optimized to generate an appropriate customer mix. Second, they need to understand various interventions at different stages of the sales funnel to move the customer from the app download to loyalty. Third, the food ordering platforms and app-based service firms need to understand how to use customer data to design customer relationship management strategies that can convert retention into referral strategies to expand the funnel.Practical implicationsIncumbents in the intensely competitive food ordering industry need to understand the process of customer acquisition and retention. An understanding of the digital sales funnel can enable them to achieve the right mix of customers in their fold. Other companies that offer app-based services can also understand various stages of the digital sales funnel, and how customers can be moved from one stage to the next one by planning appropriate interventions using app data.Originality/valueThe research contributes by offering clear insights on how food delivery platforms in India acquire and retain customers through an understanding of the digital sales funnel. The paper also offers insights on the interventions that are adopted by such firms to move customers from one stage of the sales funnel to the next one. The paper clearly brings out the value of the 3Rs for a food ordering platform – revenue, retention and referral.