Background: Tracking the performance of activities of daily living (ADLs) using ADL recognition has the potential to facilitate aging-in-place strategies, allowing older adults to live in their homes longer and enabling their families and caregivers to monitor changes in health status. However, the ADL recognition literature historically has evaluated systems in controlled settings with data from younger populations, creating the question of whether these systems will work in real-world conditions for older populations. Objective: This scoping review seeks to establish the state-of-the-art for recognizing basic ADLs using wearable sensors. This primary goal will identify literature gaps and research needed to make ADL tracking viable for aging-in-place solutions. In addition, this paper will quantify how many publications include older adults. This secondary goal assesses how often studies evaluate their system with older adult participants, enhancing the trustworthiness of the approach. Methods: We conducted a scoping review using the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines. We identify studies focused on basic ADL recognition using wearable sensors within the PubMed, Association of Computing Machinery Digital Library (ACM DL), and Google Scholar databases using papers published in the last 5 calendar years (2019-2024) to identify current trends given the rapid changes in wearable technology devices. Publications must include at least one of the basic ADLs (ie, bathing, dressing, toileting, transferring, continence, and feeding) and include some sort of wearable sensor or device. Studies focusing on instrumental ADLs, general physical activity tracking, fall detection, or only using environmental devices are excluded. Studies that include older adults in the design or evaluation of their ADL recognition system are highlighted. Results: The database search identified 695 papers; 164 papers passed title screening. A total of 58 studies satisfied the inclusion criteria; only 8 studies included older adults despite most studies identifying this population as a focus for their research. Most studies focused on eating (n=27), hygiene (n=24), drinking (n=20), or transitions (n=13). Few works included toileting (n=3), dressing (n=2), or bathing (n=1) activities. Of the 8 studies that included older adults, 5 focused on recognition performance while 3 focused on user experience and system acceptability. Conclusions: Basic ADLs are unevenly covered in the literature; more research is needed for recognizing bathing, dressing, and toileting activities. Despite all studies stating the importance of tracking ADLs in older adults, only 14% (8/58) of the included works involve older adult participants. A commonality between these outcomes is difficulty collecting or obtaining adequate training data for ADL recognition systems. Many works are predominantly concerned with proving system feasibility and do not assess usability or real-world deployment. For these systems to move from academic experiments to actual systems with clinical utility, ADL recognition systems must consider the design requirements of being part of remote health monitoring systems.

The past several decades have seen significant advances in monitoring older adults' health and well-being. However, creating viable, practical monitoring systems for informing caregivers requires understanding which behaviors and signs to track and what approaches best present that information. To investigate how technology can be leveraged to better augment caregivers' workflows, we take a multi-stage, qualitative approach to gain insights into the needs of caregivers and the older adults receiving care. Specifically, we use a series of domain expert interviews, cognitive walkthroughs, and semi-structured interviews with residents, and we synthesize our takeaways using thematic analysis at each phase. Our results show that this type of monitoring technology has great potential to reduce the effort needed by caregivers to complete their responsibilities and communicate with their teams. Additionally, we found that older adults are receptive to the technology but their privacy and autonomy must be prioritized for the sake of their mental wellbeing. These insights will facilitate greater intelligent interface development for Person-Centered Care by identifying important design considerations and vital features that require system support.

When job seekers are unsuccessful in getting a position, they often do not get feedback to inform them on how to develop a better application in the future. Therefore, there is a critical need to understand what qualifications recruiters value in order to help applicants. To address this need, we utilized eye-trackers to measure and record visual data of recruiters screening resumes to gain insight into which Areas of Interest (AOIs) influenced recruiters’ decisions the most. Using just this eye-tracking data, we trained a machine learning classifier to predict whether or not a recruiter would move a resume on to the next level of the hiring process with an AUC of 0.767. We found that features associated with recruiters looking outside the content of a resume were most predictive of their decision as well as total time viewing the resume and time spent on the Experience and Education sections. We hypothesize that this behavior is indicative of the recruiter reflecting on the content of the resume. These initial results show that applicants should focus on designing clear and concise resumes that are easy for recruiters to absorb and think about, with additional attention given to the Experience and Education sections.

Advances in wearable sensing technology are driving a new era of personalized health monitoring. In contrast to the hard, rigid form factors of conventional wearable sensors, these emerging skin-interfaced systems support high-quality physiological measurements across biophysical, biochemical, and kinematic signals of interest. These platforms enable continuous monitoring of complex physiological processes with unprecedented detail as a result of a seamless, conformal skin interface and advanced wireless communications capabilities. These platforms integrate flexible materials, miniaturized electronics, and wireless communication to provide detailed physiological data during daily activities. This review examines how skin-interfaced wearables are advancing patient care, remote monitoring, and large-scale health studies. We highlight critical barriers to clinical adoption including interpreting data, validating devices, and integration into health care systems. Key opportunities include sustainable manufacturing, point-of-care fabrication, and development of disease-specific digital biomarkers. By addressing these challenges through collaboration among engineers, clinicians, and data scientists, wearable sensors can expand patient access to advanced physiological monitoring and transform personalized medicine.

Monitoring activities of daily living (ADLs) plays an important role in measuring and responding to a person’s ability to manage their basic physical needs. Effective recognition systems for monitoring ADLs must successfully recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges associated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of preprocessing techniques to increase recall and postprocessing techniques to increase precision can result in more desirable models for tasks such as ADL monitoring. In a user-independent evaluation using in-the-wild data, these techniques resulted in a model that achieved an event-based F1-score of over 0.9 for brushing teeth, combing hair, walking, and washing hands. This work tackles fundamental challenges in machine learning that will need to be addressed in order for these systems to be deployed and reliably work in the real world.