# In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System
## 4 Discussion
### Strengths and Limitations  
A key strength of the study was that it was undertaken in real-world conditions, addressing a notable gap in the existing literature on fall detection systems. This ensured that the findings have broader applicability and are reflective of genuine user experiences. Utilizing a mixed-methods approach, the research integrated quantitative metrics, such as sensitivity and specificity, with qualitative data from user interviews. This offered a multifaceted understanding of the Neo system's performance, usability, and potential areas for improvement. The study was conducted as a collaboration between academic researchers and industry professionals. This partnership brings together the methodological rigor commonly found in academic settings with the more agile development timelines typical of the tech industry. As a result, the study adheres to stringent research standards, while also being in line with industry objectives for the evidence-based improvement of the Neo system.  
The limitations of the study warrant acknowledgment. First, the study was unable to meet its target sample size due to a confluence of factors, including COVID-19-related delays, manufacturing issues, and commercial and budgetary constraints. This limitation suggests that ongoing evaluation and algorithmic refinements could potentially improve the sensitivity and specificity values reported here. Second, technical challenges, such as devices going offline and frequent false activations, compromised data quality. Notably, 10 falls occurred while devices were offline, a missed opportunity given the rarity of fall events during the trial period. Third, despite measures like logbooks, weekly phone calls,and the exclusion of participants with known cognitive impairments, the study's reliance on self-reported events raises the possibility of underreporting. Therefore, the findings should be interpreted with caution, given the limited sample size and the potential impact of technical problems on data integrity. A further limitation is the possibility of bias being introduced by interviewer-participant familiarity. Such familiarity may have led to social desirability bias, where participants could have altered their responses to appear more favorable or agreeable. To address this, the interviewer was trained to maintain a neutral stance and encouraged participants to provide honest feedback, emphasizing the importance of candid responses for the developmental process of the Neo system. Participants were assured that all types of feedback, especially critical insights, were valuable and necessary to refine and improve the technology. It is also important to consider potential selection bias, as the participants who agreed to be interviewed may have held more favorable views toward the Neo system compared to those who declined, potentially skewing the findings. Despite these limitations, the study's findings contribute valuable insights into the Neo Care Monitoring System's performance and user experience and offer a roadmap for its future development and implementation.