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.